PROJECT 4: GLOBAL TERRORISM DATABASE¶

Applying Bayesian inference to Global Terrorism Database in order to ~predict~ terrorism

About the Dataset¶

The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2015 (with annual updates planned for the future). The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 150,000 cases. The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland.

Geography: Worldwide

Time period: 1970-2015, except 1993 (2016 in progress, publication expected June 2017)

Unit of analysis: Attack Variables: >100 variables on location, tactics, perpetrators, targets, and outcomes

Sources: Unclassified media articles (Note: Please interpret changes over time with caution. Global patterns are driven by diverse trends in particular regions, and data collection is influenced by fluctuations in access to media coverage over both time and place.)

Definition of terrorism:¶

"The threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation."

GOALS¶

(1) EDA

(2) Bayesian inference -- look at difference in incidences of terrorism across globe

(3) Impute number of bombings/explosions that occured in 1993

(4) Description of methods

(1) Exploratory Data Analysis:¶

focusing on attacktype1 -- distribution across world, frequency, etc

VIZ

REQUIRED TO PRODUCE AT LEAST ONE VIZ INDEXED BY TIME

(2) Bayesian Inference¶

Prior: amount of terror a given area has seen

Update prior with new information (e.g. new year of attacks, additional country within region)

Compare two populations using Bayesian inference.

Does amount of terror one area has seen differ significantly than that of another area (or time period)?

[For example, if you are interested in knowing if one country in South America differs in a significant way from another area, you may make your prior assume that some country is a country in South America with μ average attacks and σ variation across South American countries. You would then update that prior with the information of a single country in South America as well as a separate country in South America. How significantly do the resulting posteriors differ? (An important assumption made here is that the time periods are being held constant, perhaps a single year.)]

use credible intervals

follows pymc walkthrough

(3) Imputing Values for 1993¶

focus on attacktype1 bombings (category 3)

how to best fill in missing values?

are hemispheres separate models? (come up with better way)

apply methodology to other attack categories

turn methodology into pipeline

(4) Methods¶

Two-page (four pages double-spaced) report discussing methodology and findings. VIZ as appendices. Include (1) Bayesian test and (2) imputed valuesfor 1993 attacks. Defend prior of Bayesian inference. Justify model used to impute values for 1993.

Part 1 - Exploratory Data Analysis¶

##importing necessary libraries

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from __future__ import division
import numpy as np

from datetime import datetime
from datetime import timedelta

import pysal
import geopandas as gpd
from ipywidgets import interact, HTML, FloatSlider
from IPython.display import clear_output, display

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.arima_model import ARMA, AR
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.arima_model import ARIMA

%config InlineBackend.figure_format = 'retina'
import pymc3 as pm

Couldn't import dot_parser, loading of dot files will not be possible.

data = pd.read_csv("../../../For_DSI/GTD_Project/GTD.csv") ##reading in the csv file from the GTD

/Applications/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (4,61,62,66,116,117,123) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

##changing display parameters
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
#pd.set_option('display.max_colwidth', 800)

def EDA(dataframe): ##function for basic exploratory data analysis purposes
    print "missing values \n", dataframe.isnull().sum(), "\n"
    print "dataframe types \n", dataframe.dtypes, "\n"
    print "dataframe shape \n", dataframe.shape, "\n"
    print "dataframe describe \n", dataframe.describe(), "\n"
    print "dataframe duplicate rows \n", dataframe.duplicated().sum(), "\n"
    for item in dataframe:
        print item
        print dataframe[item].nunique()

EDA(data)

missing values 
eventid                    0
iyear                      0
imonth                     0
iday                       0
approxdate            152016
extended                   0
resolution            153270
country                    0
country_txt                0
region                     0
region_txt                 0
provstate              14520
city                     446
latitude                4519
longitude               4519
specificity                0
vicinity                   0
location              114561
summary                66140
crit1                      0
crit2                      0
crit3                      0
doubtterr                  1
alternative           132536
alternative_txt            0
multiple                   0
success                    0
suicide                    0
attacktype1                0
attacktype1_txt            0
attacktype2           151806
attacktype2_txt            0
attacktype3           156460
attacktype3_txt            0
targtype1                  0
targtype1_txt              0
targsubtype1            8155
targsubtype1_txt           0
corp1                  42576
target1                  648
natlty1                 1228
natlty1_txt                0
targtype2             147863
targtype2_txt              0
targsubtype2          148303
targsubtype2_txt           0
corp2                 148892
target2               147989
natlty2               148172
natlty2_txt                0
targtype3             155840
targtype3_txt              0
targsubtype3          155912
targsubtype3_txt           0
corp3                 155990
target3               155841
natlty3               155868
natlty3_txt                0
gname                      0
gsubname              152097
gname2                155553
gsubname2             156651
gname3                156608
ingroup                    0
ingroup2              155553
ingroup3              156608
gsubname3             156766
motive                110180
guncertain1              378
guncertain2           155610
guncertain3           156612
nperps                 71132
nperpcap               69510
claimed                66141
claimmode             143496
claimmode_txt              0
claim2                155677
claimmode2            156367
claimmode2_txt             0
claim3                156614
claimmode3            156696
claimmode3_txt             0
compclaim             152163
weaptype1                  0
weaptype1_txt              0
weapsubtype1           17907
weapsubtype1_txt           0
weaptype2             146359
weaptype2_txt              0
weapsubtype2          147477
weapsubtype2_txt           0
weaptype3             155340
weaptype3_txt              0
weapsubtype3          155466
weapsubtype3_txt           0
weaptype4             156698
weaptype4_txt              0
weapsubtype4          156701
weapsubtype4_txt           0
weapdetail             50938
nkill                   8945
nkillus                64457
nkillter               66388
nwound                 14200
nwoundus               64706
nwoundte               68310
property                   0
propextent            100420
propextent_txt             0
propvalue             125460
propcomment           107350
ishostkid                178
nhostkid              145504
nhostkidus            145559
nhours                153470
ndays                 150190
divert                156483
kidhijcountry         153482
ransom                 81680
ransomamt             155577
ransomamtus           156361
ransompaid            156149
ransompaidus          156370
ransomnote            156351
hostkidoutcome        148087
hostkidoutcome_txt         0
nreleased             148677
addnotes              134848
scite1                 66330
scite2                 95611
scite3                122640
dbsource                   0
INT_LOG                    0
INT_IDEO                   0
INT_MISC                   0
INT_ANY                    0
related               136350
dtype: int64 

dataframe types 
eventid                 int64
iyear                   int64
imonth                  int64
iday                    int64
approxdate             object
extended                int64
resolution             object
country                 int64
country_txt            object
region                  int64
region_txt             object
provstate              object
city                   object
latitude              float64
longitude             float64
specificity             int64
vicinity                int64
location               object
summary                object
crit1                   int64
crit2                   int64
crit3                   int64
doubtterr             float64
alternative           float64
alternative_txt        object
multiple                int64
success                 int64
suicide                 int64
attacktype1             int64
attacktype1_txt        object
attacktype2           float64
attacktype2_txt        object
attacktype3           float64
attacktype3_txt        object
targtype1               int64
targtype1_txt          object
targsubtype1          float64
targsubtype1_txt       object
corp1                  object
target1                object
natlty1               float64
natlty1_txt            object
targtype2             float64
targtype2_txt          object
targsubtype2          float64
targsubtype2_txt       object
corp2                  object
target2                object
natlty2               float64
natlty2_txt            object
targtype3             float64
targtype3_txt          object
targsubtype3          float64
targsubtype3_txt       object
corp3                  object
target3                object
natlty3               float64
natlty3_txt            object
gname                  object
gsubname               object
gname2                 object
gsubname2              object
gname3                 object
ingroup                 int64
ingroup2              float64
ingroup3              float64
gsubname3              object
motive                 object
guncertain1           float64
guncertain2           float64
guncertain3           float64
nperps                float64
nperpcap              float64
claimed               float64
claimmode             float64
claimmode_txt          object
claim2                float64
claimmode2            float64
claimmode2_txt         object
claim3                float64
claimmode3            float64
claimmode3_txt         object
compclaim             float64
weaptype1               int64
weaptype1_txt          object
weapsubtype1          float64
weapsubtype1_txt       object
weaptype2             float64
weaptype2_txt          object
weapsubtype2          float64
weapsubtype2_txt       object
weaptype3             float64
weaptype3_txt          object
weapsubtype3          float64
weapsubtype3_txt       object
weaptype4             float64
weaptype4_txt          object
weapsubtype4          float64
weapsubtype4_txt       object
weapdetail             object
nkill                 float64
nkillus               float64
nkillter              float64
nwound                float64
nwoundus              float64
nwoundte              float64
property                int64
propextent            float64
propextent_txt         object
propvalue             float64
propcomment            object
ishostkid             float64
nhostkid              float64
nhostkidus            float64
nhours                float64
ndays                 float64
divert                 object
kidhijcountry          object
ransom                float64
ransomamt             float64
ransomamtus           float64
ransompaid            float64
ransompaidus          float64
ransomnote             object
hostkidoutcome        float64
hostkidoutcome_txt     object
nreleased             float64
addnotes               object
scite1                 object
scite2                 object
scite3                 object
dbsource               object
INT_LOG                 int64
INT_IDEO                int64
INT_MISC                int64
INT_ANY                 int64
related                object
dtype: object 

dataframe shape 
(156772, 137) 

dataframe describe 
            eventid          iyear         imonth           iday  \
count  1.567720e+05  156772.000000  156772.000000  156772.000000   
mean   2.000541e+11    2000.474083       6.484666      15.455215   
std    1.298283e+09      12.982397       3.392225       8.815533   
min    1.970000e+11    1970.000000       0.000000       0.000000   
25%    1.989082e+11    1989.000000       4.000000       8.000000   
50%    2.005071e+11    2005.000000       6.000000      15.000000   
75%    2.013060e+11    2013.000000       9.000000      23.000000   
max    2.015123e+11    2015.000000      12.000000      31.000000   

            extended        country         region       latitude  \
count  156772.000000  156772.000000  156772.000000  152253.000000   
mean        0.041347     133.087401       6.970097      23.190988   
std         0.199091     113.946290       2.967803      19.220723   
min         0.000000       4.000000       1.000000     -53.154613   
25%         0.000000      69.000000       5.000000      10.756961   
50%         0.000000     101.000000       6.000000      31.285506   
75%         0.000000     160.000000      10.000000      34.842222   
max         1.000000    1004.000000      12.000000      74.633553   

           longitude    specificity       vicinity          crit1  \
count  152253.000000  156772.000000  156772.000000  156772.000000   
mean       24.210467       1.452632       0.067423       0.988327   
std        59.900831       1.016971       0.289041       0.107410   
min      -176.176447       1.000000      -9.000000       0.000000   
25%        -1.929857       1.000000       0.000000       1.000000   
50%        41.919647       1.000000       0.000000       1.000000   
75%        68.416974       1.000000       0.000000       1.000000   
max       179.366667       5.000000       1.000000       1.000000   

               crit2          crit3      doubtterr   alternative  \
count  156772.000000  156772.000000  156771.000000  24236.000000   
mean        0.992690       0.882919      -0.636897      1.298935   
std         0.085186       0.321518       2.621422      0.682270   
min         0.000000       0.000000      -9.000000      1.000000   
25%         1.000000       1.000000       0.000000      1.000000   
50%         1.000000       1.000000       0.000000      1.000000   
75%         1.000000       1.000000       0.000000      1.000000   
max         1.000000       1.000000       1.000000      5.000000   

            multiple        success        suicide    attacktype1  \
count  156772.000000  156772.000000  156772.000000  156772.000000   
mean        0.130374       0.903612       0.030433       3.187081   
std         0.336716       0.295124       0.171775       1.870064   
min         0.000000       0.000000       0.000000       1.000000   
25%         0.000000       1.000000       0.000000       2.000000   
50%         0.000000       1.000000       0.000000       3.000000   
75%         0.000000       1.000000       0.000000       3.000000   
max         1.000000       1.000000       1.000000       9.000000   

       attacktype2  attacktype3      targtype1   targsubtype1        natlty1  \
count  4966.000000   312.000000  156772.000000  148617.000000  155544.000000   
mean      3.615989     4.884615       8.305112      46.654885     127.635434   
std       2.214695     2.310222       6.642518      31.108047      87.606000   
min       1.000000     1.000000       1.000000       1.000000       4.000000   
25%       2.000000     2.000000       3.000000      22.000000      78.000000   
50%       2.000000     6.000000       4.000000      34.000000     104.000000   
75%       6.000000     7.000000      14.000000      74.000000     177.000000   
max       9.000000     8.000000      22.000000     111.000000    1004.000000   

         targtype2  targsubtype2      natlty2   targtype3  targsubtype3  \
count  8909.000000   8469.000000  8600.000000  932.000000    860.000000   
mean     10.117858     54.622624   130.115930    9.798283     54.862791   
std       5.764911     25.830595   122.714115    5.835194     26.779003   
min       1.000000      1.000000     4.000000    1.000000      1.000000   
25%       4.000000     29.000000    92.000000    3.000000     25.000000   
50%      14.000000     67.000000    97.000000   14.000000     67.000000   
75%      14.000000     69.000000   182.000000   14.000000     73.000000   
max      22.000000    111.000000  1004.000000   22.000000    109.000000   

           natlty3        ingroup      ingroup2      ingroup3    guncertain1  \
count   904.000000  156772.000000   1219.000000    164.000000  156394.000000   
mean    139.287611    4475.847071  19255.760459  19952.298780       0.089454   
std     152.473926   10484.350060  15913.313956  14335.366422       0.285398   
min       4.000000      -9.000000     -9.000000     -9.000000       0.000000   
25%      74.000000      -9.000000    838.000000   4292.000000       0.000000   
50%     101.000000     359.000000  20193.000000  20273.000000       0.000000   
75%     182.000000     652.000000  40021.000000  30227.000000       0.000000   
max    1004.000000  100047.000000  50011.000000  40486.000000       1.000000   

       guncertain2  guncertain3        nperps      nperpcap       claimed  \
count  1162.000000   160.000000  85640.000000  87262.000000  90631.000000   
mean      0.286575     0.262500    -61.504437     -1.572345     -0.001037   
std       0.452356     0.441374    243.824221     13.099647      1.210258   
min       0.000000     0.000000    -99.000000    -99.000000     -9.000000   
25%       0.000000     0.000000    -99.000000      0.000000      0.000000   
50%       0.000000     0.000000    -99.000000      0.000000      0.000000   
75%       1.000000     1.000000      1.000000      0.000000      0.000000   
max       1.000000     1.000000  25000.000000    406.000000      2.000000   

          claimmode       claim2  claimmode2      claim3  claimmode3  \
count  13276.000000  1095.000000  405.000000  158.000000   76.000000   
mean       7.019434     0.234703    7.387654    0.468354    7.552632   
std        2.706719     1.223132    2.990140    0.500584    3.052408   
min        0.000000    -9.000000    1.000000    0.000000    1.000000   
25%        5.000000     0.000000    7.000000    0.000000    7.000000   
50%        8.000000     0.000000    8.000000    0.000000    9.000000   
75%       10.000000     1.000000   10.000000    1.000000   10.000000   
max       10.000000     1.000000   10.000000    1.000000   10.000000   

         compclaim      weaptype1   weapsubtype1     weaptype2  weapsubtype2  \
count  4609.000000  156772.000000  138865.000000  10413.000000   9295.000000   
mean     -6.651117       6.408587      10.949519      6.626909     10.591716   
std       4.023720       2.130785       6.438986      2.085466      7.492221   
min      -9.000000       1.000000       1.000000      1.000000      1.000000   
25%      -9.000000       5.000000       5.000000      5.000000      5.000000   
50%      -9.000000       6.000000      12.000000      6.000000      7.000000   
75%       0.000000       6.000000      16.000000      8.000000     18.000000   
max       1.000000      13.000000      29.000000     13.000000     28.000000   

         weaptype3  weapsubtype3  weaptype4  weapsubtype4          nkill  \
count  1432.000000   1306.000000  74.000000     71.000000  147827.000000   
mean      6.804469     11.426493   6.243243     10.788732       2.359237   
std       2.097760      8.205943   1.497128      8.146718      11.421270   
min       2.000000      1.000000   5.000000      2.000000       0.000000   
25%       5.000000      4.000000   5.000000      3.000000       0.000000   
50%       6.000000      7.000000   6.000000      8.000000       0.000000   
75%       8.000000     19.000000   6.000000     16.000000       2.000000   
max      13.000000     28.000000  12.000000     28.000000    1500.000000   

            nkillus      nkillter         nwound      nwoundus      nwoundte  \
count  92315.000000  90384.000000  142572.000000  92066.000000  88462.000000   
mean       0.056275      0.419964       3.089926      0.045587      0.081583   
std        6.391723      3.966899      22.722313      3.440730      1.357706   
min        0.000000      0.000000       0.000000      0.000000      0.000000   
25%        0.000000      0.000000       0.000000      0.000000      0.000000   
50%        0.000000      0.000000       0.000000      0.000000      0.000000   
75%        0.000000      0.000000       2.000000      0.000000      0.000000   
max     1357.500000    500.000000    5500.000000    751.000000    200.000000   

            property    propextent     propvalue      ishostkid      nhostkid  \
count  156772.000000  56352.000000  3.131200e+04  156594.000000  11268.000000   
mean       -0.434051      3.302190  2.496208e+05       0.060060      3.962549   
std         3.031945      0.493053  1.730076e+07       0.418836    211.671702   
min        -9.000000      1.000000 -9.900000e+01      -9.000000    -99.000000   
25%         0.000000      3.000000 -9.900000e+01       0.000000      1.000000   
50%         1.000000      3.000000 -9.900000e+01       0.000000      1.000000   
75%         1.000000      4.000000  5.000000e+03       0.000000      4.000000   
max         1.000000      4.000000  2.700000e+09       1.000000  17000.000000   

         nhostkidus       nhours        ndays        ransom     ransomamt  \
count  11213.000000  3302.000000  6582.000000  75092.000000  1.195000e+03   
mean      -0.392402   -37.322229   -30.865846     -0.143664  3.320127e+06   
std        7.189533    87.837036   113.108909      1.193720  3.187694e+07   
min      -99.000000   -99.000000   -99.000000     -9.000000 -9.900000e+01   
25%        0.000000   -99.000000   -99.000000      0.000000  0.000000e+00   
50%        0.000000     0.000000     0.000000      0.000000  1.250000e+04   
75%        0.000000     0.000000     4.000000      0.000000  4.115000e+05   
max       86.000000   999.000000  1941.000000      1.000000  1.000000e+09   

        ransomamtus    ransompaid  ransompaidus  hostkidoutcome    nreleased  \
count  4.110000e+02  6.230000e+02    402.000000     8685.000000  8095.000000   
mean   5.454451e+05  4.319721e+05    305.196517        4.592170   -27.788635   
std    6.665967e+06  2.589893e+06   3409.027685        2.049184    58.524976   
min   -9.900000e+01 -9.900000e+01    -99.000000        1.000000   -99.000000   
25%    0.000000e+00 -9.900000e+01      0.000000        2.000000   -99.000000   
50%    0.000000e+00  0.000000e+00      0.000000        4.000000     0.000000   
75%    0.000000e+00  4.276840e+03      0.000000        7.000000     1.000000   
max    1.320000e+08  4.100000e+07  48000.000000        7.000000  1201.000000   

             INT_LOG       INT_IDEO       INT_MISC        INT_ANY  
count  156772.000000  156772.000000  156772.000000  156772.000000  
mean       -4.834645      -4.789114       0.093894      -4.221124  
std         4.528862       4.589779       0.602442       4.686143  
min        -9.000000      -9.000000      -9.000000      -9.000000  
25%        -9.000000      -9.000000       0.000000      -9.000000  
50%        -9.000000      -9.000000       0.000000       0.000000  
75%         0.000000       0.000000       0.000000       0.000000  
max         1.000000       1.000000       1.000000       1.000000   

dataframe duplicate rows 
0 

eventid
156772
iyear
45
imonth
13
iday
32
approxdate
1426
extended
2
resolution
2657
country
206
country_txt
206
region
12
region_txt
12
provstate
2509
city
31324
latitude
52021
longitude
51632
specificity
5
vicinity
3
location
35797
summary
88703
crit1
2
crit2
2
crit3
2
doubtterr
3
alternative
5
alternative_txt
6
multiple
2
success
2
suicide
2
attacktype1
9
attacktype1_txt
9
attacktype2
9
attacktype2_txt
10
attacktype3
8
attacktype3_txt
9
targtype1
22
targtype1_txt
22
targsubtype1
110
targsubtype1_txt
111
corp1
29295
target1
79947
natlty1
212
natlty1_txt
213
targtype2
22
targtype2_txt
23
targsubtype2
103
targsubtype2_txt
104
corp2
2344
target2
4562
natlty2
154
natlty2_txt
155
targtype3
20
targtype3_txt
21
targsubtype3
85
targsubtype3_txt
86
corp3
351
target3
631
natlty3
101
natlty3_txt
102
gname
3290
gsubname
992
gname2
334
gsubname2
43
gname3
77
ingroup
3290
ingroup2
334
ingroup3
77
gsubname3
6
motive
11682
guncertain1
2
guncertain2
2
guncertain3
2
nperps
113
nperpcap
50
claimed
4
claimmode
11
claimmode_txt
12
claim2
3
claimmode2
9
claimmode2_txt
10
claim3
2
claimmode3
8
claimmode3_txt
9
compclaim
3
weaptype1
12
weaptype1_txt
12
weapsubtype1
28
weapsubtype1_txt
29
weaptype2
11
weaptype2_txt
12
weapsubtype2
26
weapsubtype2_txt
27
weaptype3
10
weaptype3_txt
11
weapsubtype3
22
weapsubtype3_txt
23
weaptype4
5
weaptype4_txt
6
weapsubtype4
16
weapsubtype4_txt
17
weapdetail
16987
nkill
338
nkillus
30
nkillter
133
nwound
376
nwoundus
43
nwoundte
64
property
3
propextent
4
propextent_txt
5
propvalue
604
propcomment
17458
ishostkid
3
nhostkid
221
nhostkidus
28
nhours
34
ndays
289
divert
142
kidhijcountry
217
ransom
3
ransomamt
350
ransomamtus
21
ransompaid
122
ransompaidus
8
ransomnote
296
hostkidoutcome
7
hostkidoutcome_txt
8
nreleased
155
addnotes
12761
scite1
66822
scite2
50239
scite3
28554
dbsource
26
INT_LOG
3
INT_IDEO
3
INT_MISC
3
INT_ANY
3
related
20029

## no duplicate rows
##missing values for lots of columns
##event id is unique identifier for each row
##There are 13 unique months, including NA for unknown
##1993 NOT included

data.alternative_txt.unique() ##not sure what this is, but seems interesting

array(['.', 'Other Crime Type', 'Insurgency/Guerilla Action',
       'Intra/Inter-group Conflict', 'State Actors',
       'Lack of Intentionality'], dtype=object)

data.attacktype1_txt.unique()

array(['Assassination', 'Hostage Taking (Kidnapping)', 'Bombing/Explosion',
       'Facility/Infrastructure Attack', 'Armed Assault', 'Hijacking',
       'Unknown', 'Unarmed Assault', 'Hostage Taking (Barricade Incident)'], dtype=object)

data.weaptype1_txt.unique()

array(['Unknown', 'Explosives/Bombs/Dynamite', 'Incendiary', 'Firearms',
       'Chemical', 'Fake Weapons', 'Melee', 'Sabotage Equipment',
       'Vehicle (not to include vehicle-borne explosives, i.e., car or truck bombs)',
       'Radiological', 'Other', 'Biological'], dtype=object)

data.region_txt.unique() ##regions of the world represented in the dataset

array(['Central America & Caribbean', 'North America', 'Southeast Asia',
       'Western Europe', 'East Asia', 'South America', 'Eastern Europe',
       'Sub-Saharan Africa', 'Middle East & North Africa',
       'Australasia & Oceania', 'South Asia', 'Central Asia'], dtype=object)

##expanding on Mike Salmon's idea to generate random numbers for month and day where values are missing, and imputing
##those values

zeromonth = (data.imonth == 0) ##creating filters
zeroday = (data.iday == 0)

data.loc[zeromonth, "imonth"] = data.imonth.apply(lambda x: np.random.randint(1,13)) ##mapping random month values

## a function to fill in appropriate days of the month
def makemyday(month, day):
    month30 = [4, 6, 9, 11]
    month31 = [1, 3, 5, 7, 8, 10, 12]
    if month == 2:
        day = np.random.randint(1,29)
    elif month in month30:
        day = np.random.randint(1,31)
    elif month in month31:
        day = np.random.randint(1,32)
    else:
        pass
    return day

data.loc[zeroday, "iday"] = data.apply(lambda row: makemyday(row["imonth"], row["iday"]), axis = 1)

data.rename(columns={"iyear":"year", "imonth":"month", "iday":"day"}, inplace=True)

data.approxdate = pd.to_datetime(data[["year", "month", "day"]])

data["year"] = data.approxdate.dt.year

data["month"] = data.approxdate.dt.month

#data.to_csv("../../../For_DSI/GTD_Project/GTD_fixeddate.csv", index = False)

bombdata = data[["eventid", "approxdate", "year", "month", "country", "country_txt", "region", "region_txt", "city",
                 "latitude", "longitude", "targtype1", "targtype1_txt", "targsubtype1", 
                 "targsubtype1_txt", "corp1", "target1", "natlty1", "natlty1_txt", "gname",
                "weapsubtype1", "weapsubtype1_txt", "nkill", "nkillter", "nwound", "nwoundte", "propextent", "propextent_txt", "propcomment",
                 "addnotes", "scite1", "scite2", "scite3", "dbsource"]][data.attacktype1 == 3]

#bombdata.to_csv("../../../For_DSI/GTD_Project/bombdata.csv", index = False)

sns.set(style = "whitegrid")

sns.stripplot(x="region_txt", y="nkill", data=data, jitter = True)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),
 <a list of 12 Text xticklabel objects>)

sns.stripplot(x="attacktype1_txt", y="nkill", data=data, jitter = True)
plt.xticks(rotation = 90)

(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), <a list of 9 Text xticklabel objects>)

sns.countplot(x="attacktype1_txt", data=data)
plt.xticks(rotation = 90)

(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), <a list of 9 Text xticklabel objects>)

sns.countplot(x="weaptype1_txt", data=data)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),
 <a list of 12 Text xticklabel objects>)

sns.countplot(x="weapsubtype1_txt", data=bombdata)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24]),
 <a list of 25 Text xticklabel objects>)

sns.countplot(x="region_txt", data=bombdata)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),
 <a list of 12 Text xticklabel objects>)

sns.countplot(x="year", data=bombdata)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]),
 <a list of 45 Text xticklabel objects>)

g = sns.FacetGrid(bombdata, col="region_txt", col_wrap=4)
g = (g.map(plt.hist, "year"))

deadlybombs = bombdata.groupby("approxdate").nkill.agg(["sum"])

TIME VIZ:¶

dbyear = bombdata.groupby("year").nkill.agg(["sum"])

dbyear.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x13e8c5510>

deadlybombs.resample('Q').sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x13d253890>

dbmonth = deadlybombs.resample('M').sum()

dbmonth.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x11d831090>

##Tableau Visualization showing terror attacks by country, by year:
    ## https://public.tableau.com/shared/34M3H9RCR?:display_count=yes

plt.subplots(figsize=(12, 8))
sns.set(font_scale = 0.9)
sns.heatmap(bombdata.corr(), annot = True, linewidths = 0.4,
           cmap = "YlGnBu")

<matplotlib.axes._subplots.AxesSubplot at 0x13d3c8510>

g = sns.factorplot(x="year", col="region_txt", 
                   data=bombdata, kind="count", col_wrap=4)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]),
 <a list of 45 Text xticklabel objects>)

g = sns.factorplot(x="year", col="region_txt", 
                   data=data, kind="count", col_wrap=4)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]),
 <a list of 45 Text xticklabel objects>)

g = sns.factorplot(x="attacktype1_txt", col="region_txt", 
                   data=data, kind="count", col_wrap=4)

plt.xticks(rotation = 90)

(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), <a list of 9 Text xticklabel objects>)

data.groupby("region_txt").attacktype1_txt.value_counts()

region_txt                   attacktype1_txt                    
Australasia & Oceania        Bombing/Explosion                         72
                             Facility/Infrastructure Attack            52
                             Armed Assault                             47
                             Assassination                             30
                             Unknown                                   16
                             Hostage Taking (Kidnapping)               11
                             Unarmed Assault                           10
                             Hostage Taking (Barricade Incident)        5
                             Hijacking                                  3
Central America & Caribbean  Armed Assault                           4358
                             Bombing/Explosion                       3239
                             Assassination                           1252
                             Hostage Taking (Kidnapping)              501
                             Facility/Infrastructure Attack           401
                             Unknown                                  354
                             Hostage Taking (Barricade Incident)      187
                             Hijacking                                 26
                             Unarmed Assault                           19
Central Asia                 Bombing/Explosion                        224
                             Armed Assault                            112
                             Assassination                            112
                             Hostage Taking (Kidnapping)               44
                             Facility/Infrastructure Attack            19
                             Unknown                                   16
                             Hijacking                                  7
                             Unarmed Assault                            3
                             Hostage Taking (Barricade Incident)        1
East Asia                    Bombing/Explosion                        325
                             Facility/Infrastructure Attack           196
                             Armed Assault                            114
                             Assassination                             55
                             Unarmed Assault                           41
                             Unknown                                   22
                             Hijacking                                 17
                             Hostage Taking (Kidnapping)               14
                             Hostage Taking (Barricade Incident)        2
Eastern Europe               Bombing/Explosion                       2665
                             Armed Assault                           1225
                             Assassination                            374
                             Facility/Infrastructure Attack           222
                             Hostage Taking (Kidnapping)              201
                             Unknown                                  111
                             Unarmed Assault                           51
                             Hijacking                                 26
                             Hostage Taking (Barricade Incident)       17
Middle East & North Africa   Bombing/Explosion                      24053
                             Armed Assault                           8057
                             Assassination                           3889
                             Hostage Taking (Kidnapping)             2029
                             Unknown                                 1133
                             Facility/Infrastructure Attack           920
                             Unarmed Assault                          144
                             Hijacking                                118
                             Hostage Taking (Barricade Incident)       79
North America                Bombing/Explosion                       1518
                             Facility/Infrastructure Attack           833
                             Armed Assault                            387
                             Assassination                            231
                             Hostage Taking (Kidnapping)              120
                             Unarmed Assault                           68
                             Hostage Taking (Barricade Incident)       61
                             Unknown                                   32
                             Hijacking                                 18
South America                Bombing/Explosion                       8931
                             Armed Assault                           3797
                             Assassination                           2705
                             Hostage Taking (Kidnapping)             1358
                             Facility/Infrastructure Attack           754
                             Unknown                                  742
                             Hostage Taking (Barricade Incident)      228
                             Hijacking                                 66
                             Unarmed Assault                           47
South Asia                   Bombing/Explosion                      18247
                             Armed Assault                           9750
                             Assassination                           3707
                             Hostage Taking (Kidnapping)             2653
                             Facility/Infrastructure Attack          1702
                             Unknown                                 1385
                             Unarmed Assault                          241
                             Hijacking                                 83
                             Hostage Taking (Barricade Incident)       73
Southeast Asia               Bombing/Explosion                       4019
                             Armed Assault                           3450
                             Assassination                           1060
                             Facility/Infrastructure Attack           761
                             Hostage Taking (Kidnapping)              603
                             Unknown                                  374
                             Hostage Taking (Barricade Incident)       37
                             Hijacking                                 32
                             Unarmed Assault                           24
Sub-Saharan Africa           Armed Assault                           4671
                             Bombing/Explosion                       4316
                             Hostage Taking (Kidnapping)             1315
                             Assassination                           1265
                             Unknown                                 1046
                             Facility/Infrastructure Attack           598
                             Hijacking                                 99
                             Unarmed Assault                           63
                             Hostage Taking (Barricade Incident)       61
Western Europe               Bombing/Explosion                       8354
                             Assassination                           2902
                             Facility/Infrastructure Attack          2391
                             Armed Assault                           1586
                             Hostage Taking (Kidnapping)              266
                             Unknown                                  259
                             Unarmed Assault                          117
                             Hostage Taking (Barricade Incident)       84
                             Hijacking                                 61
Name: attacktype1_txt, dtype: int64

g = sns.factorplot(x="year", col="attacktype1_txt", 
                   data=data, kind="count", col_wrap=4)

plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]),
 <a list of 45 Text xticklabel objects>)

data.groupby("year").attacktype1_txt.value_counts()

year  attacktype1_txt                    
1970  Bombing/Explosion                       333
      Facility/Infrastructure Attack          174
      Armed Assault                            61
      Hostage Taking (Kidnapping)              38
      Assassination                            22
      Hijacking                                11
      Unknown                                   6
      Hostage Taking (Barricade Incident)       3
      Unarmed Assault                           3
1971  Bombing/Explosion                       238
      Facility/Infrastructure Attack           88
      Assassination                            70
      Armed Assault                            44
      Hostage Taking (Kidnapping)              20
      Hijacking                                 6
      Unknown                                   3
      Hostage Taking (Barricade Incident)       1
1972  Assassination                           193
      Bombing/Explosion                       186
      Armed Assault                            63
      Facility/Infrastructure Attack           19
      Hostage Taking (Kidnapping)              16
      Hijacking                                12
      Hostage Taking (Barricade Incident)       4
      Unknown                                   1
1973  Assassination                           164
      Bombing/Explosion                       149
      Armed Assault                            62
      Hostage Taking (Kidnapping)              43
      Facility/Infrastructure Attack           36
      Hijacking                                 8
      Hostage Taking (Barricade Incident)       7
      Unarmed Assault                           3
      Unknown                                   1
1974  Bombing/Explosion                       284
      Assassination                           158
      Armed Assault                            46
      Facility/Infrastructure Attack           42
      Hostage Taking (Kidnapping)              37
      Hostage Taking (Barricade Incident)       5
      Unarmed Assault                           4
      Hijacking                                 3
      Unknown                                   1
1975  Bombing/Explosion                       370
      Assassination                           181
      Armed Assault                            81
      Facility/Infrastructure Attack           64
      Hostage Taking (Kidnapping)              27
      Hostage Taking (Barricade Incident)      13
      Unknown                                   3
      Hijacking                                 1
1976  Bombing/Explosion                       419
      Assassination                           204
      Armed Assault                           124
      Facility/Infrastructure Attack          113
      Hostage Taking (Kidnapping)              45
      Hostage Taking (Barricade Incident)       6
      Unknown                                   5
      Hijacking                                 4
      Unarmed Assault                           3
1977  Bombing/Explosion                       635
      Armed Assault                           255
      Facility/Infrastructure Attack          182
      Assassination                           146
      Hostage Taking (Kidnapping)              67
      Unknown                                  14
      Hostage Taking (Barricade Incident)      13
      Hijacking                                 7
1978  Bombing/Explosion                       644
      Assassination                           263
                                             ... 
2008  Hostage Taking (Kidnapping)             392
      Facility/Infrastructure Attack          285
      Assassination                           219
      Unknown                                 106
      Unarmed Assault                          33
      Hijacking                                15
      Hostage Taking (Barricade Incident)       2
2009  Bombing/Explosion                      2565
      Armed Assault                          1118
      Facility/Infrastructure Attack          349
      Hostage Taking (Kidnapping)             279
      Assassination                           227
      Unknown                                 137
      Hijacking                                23
      Unarmed Assault                          19
      Hostage Taking (Barricade Incident)       4
2010  Bombing/Explosion                      2507
      Armed Assault                          1114
      Hostage Taking (Kidnapping)             390
      Assassination                           360
      Facility/Infrastructure Attack          293
      Unknown                                 116
      Unarmed Assault                          21
      Hijacking                                13
      Hostage Taking (Barricade Incident)       7
2011  Bombing/Explosion                      2587
      Armed Assault                          1391
      Hostage Taking (Kidnapping)             361
      Assassination                           342
      Facility/Infrastructure Attack          242
      Unknown                                 113
      Unarmed Assault                          20
      Hijacking                                 9
      Hostage Taking (Barricade Incident)       2
2012  Bombing/Explosion                      5080
      Armed Assault                          2081
      Assassination                           458
      Hostage Taking (Kidnapping)             352
      Facility/Infrastructure Attack          293
      Unknown                                 182
      Unarmed Assault                          29
      Hostage Taking (Barricade Incident)      18
      Hijacking                                 5
2013  Bombing/Explosion                      6669
      Armed Assault                          2959
      Assassination                           845
      Hostage Taking (Kidnapping)             628
      Facility/Infrastructure Attack          555
      Unknown                                 253
      Unarmed Assault                          38
      Hostage Taking (Barricade Incident)      26
      Hijacking                                17
2014  Bombing/Explosion                      8759
      Armed Assault                          4087
      Hostage Taking (Kidnapping)            1396
      Assassination                           922
      Unknown                                 782
      Facility/Infrastructure Attack          764
      Hijacking                                48
      Hostage Taking (Barricade Incident)      45
      Unarmed Assault                          37
2015  Bombing/Explosion                      7615
      Armed Assault                          3353
      Hostage Taking (Kidnapping)            1175
      Assassination                           917
      Unknown                                 855
      Facility/Infrastructure Attack          682
      Unarmed Assault                          96
      Hostage Taking (Barricade Incident)      76
      Hijacking                                37
Name: attacktype1_txt, dtype: int64

data.groupby("year").nkill.agg(["sum"]).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x144202290>

data.groupby("year").nwound.agg(["sum"]).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1442d2b90>

Part 3 - IMPUTING 1993¶

data["is_bombing"] = [1 if (type == 3) else 0 for type in data.attacktype1]

data.is_bombing.value_counts() ##There is a 48% chance that an attack was a bombing

0    80809
1    75963
Name: is_bombing, dtype: int64

y = data.is_bombing

data.corr()

tsdf = data[["approxdate", "is_bombing"]]

tsdf2 = tsdf.groupby("approxdate").is_bombing.agg(["sum"])

tsdf2.resample("M").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x145d4b310>

tsdf2["sum"].resample("M").sum().autocorr(lag=12)

0.88878939261556356

pd.rolling_mean(tsdf2, window=90, center=True).plot()

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(window=90,center=True).mean()
  if __name__ == '__main__':

<matplotlib.axes._subplots.AxesSubplot at 0x126656950>

tsdf2["change_in_numattacks"] = tsdf2["sum"].diff(periods = 1)

tsdf2.change_in_numattacks.resample("BA").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x146f85610>

tsdf2["sum"].resample("BA").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1471cdad0>

bombsperyear = tsdf2[["sum"]].resample("A").sum()

bombsperyear.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x142a69d10>

pizza = tsdf2.loc["1991":"1995",:]

pizza["sum"].resample("M").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x14c9d2510>

pizza["change_in_numattacks"].resample("M").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x14c81af10>

pizza1 = tsdf2.loc["1970":"1992",:]
pizza2 = tsdf2.loc["1994":"2015",:]

pizza1["sum"].resample("M").sum().autocorr(lag = 12)

0.76047781547228566

pizza2["sum"].resample("M").sum().autocorr(lag = -12)

0.88503075768447148

pizza1["sum"].resample("A").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x14d3fc510>

pizza1["sum"].resample("A").sum().autocorr(lag = 1)

0.95255326438331178

pizza2["sum"].resample("A").sum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x14d3af890>

pizza2["sum"].resample("A").sum().autocorr(lag = -1)

0.94126076572961992

pizza1["sum"].resample("A").sum().plot()
pizza2["sum"].resample("A").sum().plot()
plt.show()

plot_acf(pizza1["sum"].resample("A").sum(), lags = 20)
plot_pacf(pizza1["sum"].resample("A").sum(), lags = 20)
plot_acf(pizza2["sum"].resample("A").sum(), lags = 20)
plot_pacf(pizza2["sum"].resample("A").sum(), lags = 20)



plt.show()

##use two-year periods for prediction

oldpizza = pizza1[["sum"]].resample("A").sum().astype(float)

newpizza = pizza2[["sum"]].resample("A").sum().astype(float)

plot_acf(pizza1["change_in_numattacks"].resample("A").sum(), lags = 20)
plot_pacf(pizza1["change_in_numattacks"].resample("A").sum(), lags = 20)
plot_acf(pizza2["change_in_numattacks"].resample("A").sum(), lags = 20)
plot_pacf(pizza2["change_in_numattacks"].resample("A").sum(), lags = 20)



plt.show()

ovalues = oldpizza['sum'].values
odates = oldpizza.index

model = ARMA(oldpizza, (2,0)).fit()
model.summary()

plot_acf(model.resid, lags=20)

model.predict()

1970-12-31    1044.919285
1971-12-31     362.179502
1972-12-31     281.246659
1973-12-31     223.963769
1974-12-31     185.766941
1975-12-31     287.886364
1976-12-31     378.990314
1977-12-31     432.390280
1978-12-31     613.556558
1979-12-31     656.254438
1980-12-31     988.738146
1981-12-31    1008.498271
1982-12-31    1066.765965
1983-12-31    1115.192858
1984-12-31    1219.234244
1985-12-31    1663.087400
1986-12-31    1515.162698
1987-12-31    1486.263539
1988-12-31    1466.162323
1989-12-31    1600.688860
1990-12-31    1747.089153
1991-12-31    1718.341746
1992-12-31    1913.475790
Freq: A-DEC, dtype: float64

model.resid.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x14dc0eb50>

newpizza.sort_index(ascending = False, inplace = True)

newpizza.head()

model2 = ARMA(newpizza, (2,0)).fit()
model2.summary()

plot_acf(model2.resid, lags=20)
plt.show()

model.resid

approxdate
1970-12-31   -711.919285
1971-12-31   -124.179502
1972-12-31    -95.246659
1973-12-31    -74.963769
1974-12-31     98.233059
1975-12-31     82.113636
1976-12-31     40.009686
1977-12-31    202.609720
1978-12-31     30.443442
1979-12-31    400.745562
1980-12-31      8.261854
1981-12-31     73.501729
1982-12-31     58.234035
1983-12-31    130.807142
1984-12-31    555.765756
1985-12-31   -181.087400
1986-12-31     -9.162698
1987-12-31    -10.263539
1988-12-31    183.837677
1989-12-31    196.311140
1990-12-31    -16.089153
1991-12-31    269.658254
1992-12-31   -175.475790
Freq: A-DEC, dtype: float64

model2.resid

approxdate
2015-12-31    4913.125727
2014-12-31    1522.791896
2013-12-31   -1967.504776
2012-12-31     -64.006655
2011-12-31   -1407.788737
2010-12-31    1175.204115
2009-12-31      76.038538
2008-12-31      31.564102
2007-12-31    -823.146720
2006-12-31     -64.975259
2005-12-31    -392.867437
2004-12-31    -376.655820
2003-12-31      -6.991231
2002-12-31    -204.139604
2001-12-31    -171.314476
2000-12-31    -160.228163
1999-12-31    -513.048454
1998-12-31    -258.229941
1997-12-31     438.632732
1996-12-31    -404.648667
1995-12-31    -646.566548
1994-12-31     356.480042
Freq: -1A-DEC, dtype: float64

model2.resid.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x14f0fef90>

tsdf2.head()

tsdf.head()

#tsdf2 = tsdf.groupby("approxdate").is_bombing.agg(["sum"])

#tsdf2["yearmonth"] = tsdf2.index.resample("M")

#df['mnth_yr'] = df['date_column'].apply(lambda x: x.strftime('%B-%Y'))

tsdf["yearmonth"] = tsdf.approxdate.apply(lambda x: x.strftime("%B-%Y"))

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

tsdf["yearmonth"] = pd.to_datetime(tsdf.yearmonth)

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

tsdf.dtypes

approxdate    datetime64[ns]
is_bombing             int64
yearmonth     datetime64[ns]
dtype: object

tsdf3 = tsdf.groupby("yearmonth").is_bombing.agg(["sum"])

tsdf2.resample("M").sum()

tsdf3["sum"] = tsdf3["sum"].astype(float)

tsdf3["change"] = tsdf3["sum"].diff(periods = 1)

tsdf3.head()

decomposition = seasonal_decompose(tsdf3["sum"], freq=12)  
fig = plt.figure()  
fig = decomposition.plot()  
fig.set_size_inches(15, 8)

<matplotlib.figure.Figure at 0x14f0fe210>

decomposition = seasonal_decompose(oldpizza["sum"], freq=2)  
fig = plt.figure()  
fig = decomposition.plot()  
fig.set_size_inches(15, 8)

<matplotlib.figure.Figure at 0x148873c10>

decomposition = seasonal_decompose(newpizza["sum"], freq=2)  
fig = plt.figure()  
fig = decomposition.plot()  
fig.set_size_inches(15, 8)

<matplotlib.figure.Figure at 0x14946df50>

decomposition = seasonal_decompose(tsdf2["sum"], freq=365)  
fig = plt.figure()  
fig = decomposition.plot()  
fig.set_size_inches(15, 8)

<matplotlib.figure.Figure at 0x150008cd0>

def test_stationarity(timeseries):

    #Determing rolling statistics
    rolmean = pd.rolling_mean(timeseries, window=12)
    rolstd = pd.rolling_std(timeseries, window=12)

    #Plot rolling statistics:
    fig = plt.figure(figsize=(12, 8))
    orig = plt.plot(timeseries, color='blue',label='Original')
    mean = plt.plot(rolmean, color='red', label='Rolling Mean')
    std = plt.plot(rolstd, color='black', label = 'Rolling Std')
    plt.legend(loc='best')
    plt.title('Rolling Mean & Standard Deviation')
    plt.show()
    
    #Perform Dickey-Fuller test:
    print 'Results of Dickey-Fuller Test:'
    dftest = adfuller(timeseries, autolag='AIC')
    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
    for key,value in dftest[4].items():
        dfoutput['Critical Value (%s)'%key] = value
    print dfoutput

test_stationarity(tsdf3["sum"])

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).mean()
/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).std()

Results of Dickey-Fuller Test:
Test Statistic                   0.445415
p-value                          0.983116
#Lags Used                      11.000000
Number of Observations Used    528.000000
Critical Value (5%)             -2.867030
Critical Value (1%)             -3.442796
Critical Value (10%)            -2.569694
dtype: float64

test_stationarity(tsdf3.change.dropna(inplace=False))

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).mean()
/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).std()

Results of Dickey-Fuller Test:
Test Statistic                -7.339097e+00
p-value                        1.076937e-10
#Lags Used                     1.100000e+01
Number of Observations Used    5.270000e+02
Critical Value (5%)           -2.867040e+00
Critical Value (1%)           -3.442820e+00
Critical Value (10%)          -2.569699e+00
dtype: float64

tsdf3["SAD"] = tsdf3["sum"] - tsdf3["sum"].shift(12)  
test_stationarity(tsdf3["SAD"].dropna(inplace=False))

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).mean()
/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).std()

Results of Dickey-Fuller Test:
Test Statistic                  -3.533627
p-value                          0.007164
#Lags Used                      15.000000
Number of Observations Used    512.000000
Critical Value (5%)             -2.867202
Critical Value (1%)             -3.443187
Critical Value (10%)            -2.569785
dtype: float64

tsdf3["SADchange"] = tsdf3.change - tsdf3.change.shift(12)  
test_stationarity(tsdf3.SADchange.dropna(inplace=False))

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).mean()
/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).std()

Results of Dickey-Fuller Test:
Test Statistic                -7.874770e+00
p-value                        4.877171e-12
#Lags Used                     1.900000e+01
Number of Observations Used    5.070000e+02
Critical Value (5%)           -2.867258e+00
Critical Value (1%)           -3.443314e+00
Critical Value (10%)          -2.569815e+00
dtype: float64

ts_log = np.log(tsdf3["sum"])

plt.plot(ts_log)

[<matplotlib.lines.Line2D at 0x14bb4e310>]

ts_log_diff = ts_log - ts_log.shift()
plt.plot(ts_log_diff)

[<matplotlib.lines.Line2D at 0x14cbb9c10>]

ts_log_diff.dropna(inplace=True)
test_stationarity(ts_log_diff)

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).mean()
/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).std()

Results of Dickey-Fuller Test:
Test Statistic                -1.110970e+01
p-value                        3.686433e-20
#Lags Used                     8.000000e+00
Number of Observations Used    5.300000e+02
Critical Value (5%)           -2.867009e+00
Critical Value (1%)           -3.442749e+00
Critical Value (10%)          -2.569683e+00
dtype: float64

##log difference is stationary with 99% confidence

lag_acf = acf(ts_log_diff, nlags=20)
lag_pacf = pacf(ts_log_diff, nlags=20, method='ols')

plt.subplot(121) 
plt.plot(lag_acf)
plt.axhline(y=0,linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.title('Autocorrelation Function')

<matplotlib.text.Text at 0x14f0d2290>

# p – The lag value where the PACF chart crosses the upper confidence interval 
# for the first time. 
# q – The lag value where the ACF chart crosses the upper confidence interval 
# for the first time.

## p = 1, q = 1, d = 1

plt.subplot(122)
plt.plot(lag_pacf)
plt.axhline(y=0,linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.title('Partial Autocorrelation Function')
plt.tight_layout()

model = ARIMA(ts_log, order=(1, 1, 1))  
results_ARIMA = model.fit(disp=-1)  
plt.plot(ts_log_diff)
plt.plot(results_ARIMA.fittedvalues, color='red')

[<matplotlib.lines.Line2D at 0x14f573fd0>]

predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)
print predictions_ARIMA_diff.head()

yearmonth
1970-02-01    0.005805
1970-03-01   -0.372597
1970-04-01   -0.376705
1970-05-01   -0.320884
1970-06-01    0.154951
dtype: float64

predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()
predictions_ARIMA_log = pd.Series(ts_log.ix[0], index=ts_log.index)
predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,fill_value=0)
predictions_ARIMA_log.head()

yearmonth
1970-01-01    2.708050
1970-02-01    2.713855
1970-03-01    2.341258
1970-04-01    1.964553
1970-05-01    1.643669
dtype: float64

predictions_ARIMA = np.exp(predictions_ARIMA_log)
plt.plot(tsdf3["sum"])
plt.plot(predictions_ARIMA)

[<matplotlib.lines.Line2D at 0x14cd7f350>]

tsdf3["predict"] = predictions_ARIMA

tsdf3.loc["1994"]

PART1 = tsdf3.loc["1970":"1992",:]
PART2 = tsdf3.loc["1994":"2015",:]

ts_log = np.log(PART1["sum"])

plt.plot(ts_log)

[<matplotlib.lines.Line2D at 0x14f8632d0>]

ts_log_diff = ts_log - ts_log.shift()
plt.plot(ts_log_diff)

[<matplotlib.lines.Line2D at 0x150247290>]

ts_log_diff.dropna(inplace=True)
test_stationarity(ts_log_diff)

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).mean()
/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=12,center=False).std()

Results of Dickey-Fuller Test:
Test Statistic                -8.859181e+00
p-value                        1.499006e-14
#Lags Used                     8.000000e+00
Number of Observations Used    2.660000e+02
Critical Value (5%)           -2.872468e+00
Critical Value (1%)           -3.455175e+00
Critical Value (10%)          -2.572593e+00
dtype: float64

plt.subplot(121) 
plt.plot(lag_acf)
plt.axhline(y=0,linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.title('Autocorrelation Function')

<matplotlib.text.Text at 0x151a51c50>

plt.subplot(122)
plt.plot(lag_pacf)
plt.axhline(y=0,linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.title('Partial Autocorrelation Function')
plt.tight_layout()

model = ARIMA(ts_log, order=(1, 0, 0))  
results_ARIMA = model.fit(disp=-1)  
plt.plot(ts_log)
plt.plot(results_ARIMA.fittedvalues, color='red')

[<matplotlib.lines.Line2D at 0x15440ded0>]

predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)
#predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()
#predictions_ARIMA_log = pd.Series(ts_log.ix[0], index=ts_log.index)
#predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,fill_value=0)
predictions_ARIMA = np.exp(predictions_ARIMA_diff)
plt.plot(PART1["sum"])
plt.plot(predictions_ARIMA)

[<matplotlib.lines.Line2D at 0x1549b1290>]

PART1["predict"] = predictions_ARIMA
PART1.loc["1992"]

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

PART1.loc["1992","sum"].sum()

1738.0

PART1.loc["1991","sum"].sum()

1988.0

PART1.loc["1992","predict"].sum()

1493.1261835456676

PART1.loc["1991","predict"].sum()

1782.5481332700003

results_ARIMA.summary()

fig, ax = plt.subplots(figsize=(10,8))
fig = results_ARIMA.plot_predict(start = "1990-01-01", end = "1995-12-01", ax = ax)
legend = ax.legend(loc='upper left')

rl = results_ARIMA.predict(start = "1990-01-01", end = "1995-12-01")

rls = pd.Series(rl, copy = True)

fillitin = np.exp(rls)

quiche = tsdf3.loc["1990":"1995",:]

fig, ax = plt.subplots(figsize=(10,8))
plt.plot(quiche["sum"])
plt.plot(fillitin)
legend = ax.legend(loc='upper left')

quiche.loc["1992"]["sum"].sum()

1738.0

impute = pd.DataFrame(fillitin, columns = ["prediction"])

impute.loc["1992"].prediction.sum()

1493.1261835456676

impute.loc["1993"].prediction.sum()

1495.2652135616154

impute.loc["1994"].prediction.sum()

906.0012048521072

quiche.loc["1994"]["sum"].sum()

1153.0

ARIMA MODEL PREDICTS 1495 BOMBINGS IN 1993¶

PART 2 - Bayesian Inference¶

data[data.year == 1990].shape

(3887, 138)

data.groupby("attacktype1_txt").year.value_counts() ##877 assassinations in 1990, 3887 attacks

attacktype1_txt  year
Armed Assault    2014    4087
                 2015    3353
                 2013    2959
                 2012    2081
                 2011    1391
                 1992    1327
                 1991    1271
                 1989    1120
                 2009    1118
                 2010    1114
                 2008    1093
                 1988     921
                 1990     876
                 1983     852
                 2007     848
                 1997     826
                 1984     823
                 1994     818
                 1987     798
                 1995     740
                 2006     737
                 1981     697
                 1982     665
                 1985     659
                 1996     638
                 1986     592
                 2001     581
                 1980     574
                 2005     510
                 2000     492
                 1979     447
                 1999     368
                 2002     366
                 2003     312
                 1998     264
                 2004     259
                 1977     255
                 1978     241
                 1976     124
                 1975      81
                 1972      63
                 1973      62
                 1970      61
                 1974      46
                 1971      44
Assassination    1992    1111
                 1989     980
                 2014     922
                 2015     917
                 1990     877
                 2013     845
                 1988     821
                 1994     771
                 1991     730
                 1995     729
                 1980     618
                 1979     526
                 1987     495
                 1996     478
                 2012     458
                 1984     443
                 1997     420
                 1981     405
                 1986     371
                 1982     362
                 1983     360
                 2010     360
                 2011     342
                 1985     311
                 1978     263
                         ... 
Unarmed Assault  2009      19
                 1998      15
                 2006      15
                 1991      14
                 2002      10
                 1979       7
                 1984       7
                 1989       7
                 1990       7
                 1986       6
                 2005       6
                 2007       6
                 1978       5
                 1985       5
                 1988       5
                 1974       4
                 1987       4
                 2004       4
                 1970       3
                 1973       3
                 1976       3
                 1981       3
                 1982       3
                 1980       2
                 1983       2
Unknown          2015     855
                 2014     782
                 1997     348
                 1996     291
                 2013     253
                 1995     232
                 1994     204
                 1979     196
                 1992     188
                 2012     182
                 1987     164
                 1984     157
                 2009     137
                 1985     134
                 1986     117
                 2010     116
                 2011     113
                 1983     110
                 2008     106
                 1980      98
                 1982      79
                 1981      74
                 1991      64
                 1978      52
                 2001      45
                 2000      43
                 1988      39
                 1999      39
                 2006      39
                 2005      38
                 2007      30
                 2003      27
                 2004      26
                 2002      25
                 1998      23
                 1989      17
                 1977      14
                 1990      13
                 1970       6
                 1976       5
                 1971       3
                 1975       3
                 1972       1
                 1973       1
                 1974       1
Name: year, dtype: int64

##Looking only at 1990

nineoh = data[data.year == 1990]

g = sns.factorplot(x="attacktype1_txt", col="region_txt", 
                   data=nineoh, kind="count", col_wrap=4)

plt.xticks(rotation = 90)

(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), <a list of 9 Text xticklabel objects>)

g = sns.factorplot(x="region_txt", col="attacktype1_txt", 
                   data=nineoh, kind="count", col_wrap=4)

plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),
 <a list of 11 Text xticklabel objects>)

bae = data[data.region_txt =="Sub-Saharan Africa"][data.year == 1990]

/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  if __name__ == '__main__':

pd.set_option('display.max_rows', 50)
bae

bae2 = bae[bae.country_txt != "South Africa"]
bae2 = bae2[bae2.country_txt != "Uganda"]

bae.country_txt.value_counts()

Angola                   205
South Africa             154
Mozambique                23
Uganda                    14
Somalia                   12
Senegal                   11
Ethiopia                  10
Zimbabwe                   6
Zambia                     4
Liberia                    4
Namibia                    2
Botswana                   1
Djibouti                   1
Republic of the Congo      1
Mali                       1
Sudan                      1
Name: country_txt, dtype: int64

bae2.country_txt.value_counts()

Angola                   205
Mozambique                23
Somalia                   12
Senegal                   11
Ethiopia                  10
Zimbabwe                   6
Zambia                     4
Liberia                    4
Namibia                    2
Botswana                   1
Mali                       1
Djibouti                   1
Republic of the Congo      1
Sudan                      1
Name: country_txt, dtype: int64

SA = bae[bae.country_txt == "South Africa"]

SA.attacktype1_txt.value_counts()

Bombing/Explosion                 67
Armed Assault                     34
Assassination                     31
Facility/Infrastructure Attack    21
Hostage Taking (Kidnapping)        1
Name: attacktype1_txt, dtype: int64

sa = bae[bae.country_txt == "South Africa"].nwound.values

ug = bae[bae.country_txt == "Uganda"].nwound.values

mean_prior_mean = bae2.nwound.mean()
mean_prior_std = bae2.nwound.std()

# model specifications in PyMC3 are wrapped in a with-statement
with pm.Model() as model:

    groupSA_mean = pm.Normal('Wounded_SA_mean', mean_prior_mean, sd=mean_prior_std)
    groupUG_mean = pm.Normal('Wounded_UG_mean', mean_prior_mean, sd=mean_prior_std)

std_prior_lower = 0.01
std_prior_upper = 100.0

with model:
    
    groupSA_std = pm.Uniform('Wounded_SA_std', lower=std_prior_lower, upper=std_prior_upper)
    groupUG_std = pm.Uniform('Wounded_UG_std', lower=std_prior_lower, upper=std_prior_upper)

with model:

    groupSA = pm.Normal('Wounded_SA', mu=groupSA_mean, sd=groupSA_std, observed=sa)
    groupUG = pm.Normal('Wounded_UG', mu=groupUG_mean, sd=groupUG_std, observed=ug)

with model:

    diff_of_means = pm.Deterministic('difference of means', groupSA_mean - groupUG_mean)
    diff_of_stds = pm.Deterministic('difference of stds', groupSA_std - groupUG_std)
    effect_size = pm.Deterministic('effect size',
                                   diff_of_means / np.sqrt((groupSA_std**2 + groupUG_std**2) / 2))

with model:
    trace = pm.sample(25000, njobs=-1)

Auto-assigning NUTS sampler...
Initializing NUTS using advi...
Average ELBO = -614.48: 100%|██████████| 200000/200000 [00:30<00:00, 6465.19it/s]
Finished [100%]: Average ELBO = -614.48
100%|██████████| 25000/25000 [00:40<00:00, 612.57it/s]

pm.plot_posterior(trace[3000:],
                  varnames=['Wounded_SA_mean', 'Wounded_UG_mean', 'Wounded_SA_std', 'Wounded_UG_std'],
                  color='#87ceeb')

array([<matplotlib.axes._subplots.AxesSubplot object at 0x169df7290>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x169df7b50>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x16960c790>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x169443810>], dtype=object)

pm.plot_posterior(trace[3000:],
                  varnames=['difference of means', 'difference of stds', 'effect size'],
                  ref_val=0,
                  color='#87ceeb')

array([<matplotlib.axes._subplots.AxesSubplot object at 0x1644d6c10>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1663c77d0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x16651c6d0>], dtype=object)

pm.summary(trace[3000:],
           varnames=['difference of means', 'difference of stds', 'effect size'])

difference of means:

  Mean             SD               MC Error         95% HPD interval
  -------------------------------------------------------------------
  
  -0.313           1.767            0.013            [-3.717, 3.250]

  Posterior quantiles:
  2.5            25             50             75             97.5
  |--------------|==============|==============|--------------|
  
  -3.815         -1.463         -0.319         0.828          3.173


difference of stds:

  Mean             SD               MC Error         95% HPD interval
  -------------------------------------------------------------------
  
  3.192            1.435            0.012            [0.222, 5.681]

  Posterior quantiles:
  2.5            25             50             75             97.5
  |--------------|==============|==============|--------------|
  
  -0.129         2.412          3.364          4.180          5.507


effect size:

  Mean             SD               MC Error         95% HPD interval
  -------------------------------------------------------------------
  
  -0.040           0.222            0.002            [-0.476, 0.396]

  Posterior quantiles:
  2.5            25             50             75             97.5
  |--------------|==============|==============|--------------|
  
  -0.479         -0.188         -0.041         0.106          0.395

##When looking at casualty rates due to terror attacks in Sub-Saharan Africa for the year 1990,
##South Africa does not differ significantly from Uganda -- only the spread of the data differs significantly

	eventid	year	month	day	extended	country	region	latitude	longitude	specificity	vicinity	crit1	crit2	crit3	doubtterr	alternative	multiple	success	suicide	attacktype1	attacktype2	attacktype3	targtype1	targsubtype1	natlty1	targtype2	targsubtype2	natlty2	targtype3	targsubtype3	natlty3	ingroup	ingroup2	ingroup3	guncertain1	guncertain2	guncertain3	nperps	nperpcap	claimed	claimmode	claim2	claimmode2	claim3	claimmode3	compclaim	weaptype1	weapsubtype1	weaptype2	weapsubtype2	weaptype3	weapsubtype3	weaptype4	weapsubtype4	nkill	nkillus	nkillter	nwound	nwoundus	nwoundte	property	propextent	propvalue	ishostkid	nhostkid	nhostkidus	nhours	ndays	ransom	ransomamt	ransomamtus	ransompaid	ransompaidus	hostkidoutcome	nreleased	INT_LOG	INT_IDEO	INT_MISC	INT_ANY	is_bombing
eventid	1.000000	0.999996	0.010205	0.007057	0.088933	-0.144918	0.391129	0.172221	0.553091	0.029533	0.077430	0.000168	0.024546	0.008004	0.278633	0.063645	0.091466	-0.049866	0.134558	0.048222	0.030693	0.054855	0.068833	0.047099	-0.023637	0.096598	0.113583	-0.104678	0.149229	0.081981	0.004898	0.320870	0.467095	0.492234	0.079657	-0.056744	-0.137790	-0.137350	0.473824	0.126256	0.115381	0.030646	0.124254	-0.417761	0.082421	0.272146	-0.038496	0.100979	0.049107	0.029094	0.050598	0.083092	-0.136695	0.031511	0.013383	-0.013713	0.015116	0.031025	-0.030874	0.019343	-0.295301	-0.154098	-0.012391	-0.010183	0.004759	-0.018742	-0.455918	-0.188772	-0.271519	-0.002849	-0.000461	-0.169650	-0.172339	0.292161	-0.228536	-0.234006	-0.231593	-0.077012	-0.266880	0.092875
year	0.999996	1.000000	0.007585	0.006978	0.088923	-0.144894	0.391142	0.172276	0.553111	0.029520	0.077450	0.000140	0.024544	0.008009	0.278671	0.063702	0.091481	-0.049852	0.134543	0.048197	0.030941	0.055001	0.068837	0.047106	-0.023609	0.096492	0.113482	-0.104635	0.149068	0.081870	0.004864	0.320863	0.466974	0.491812	0.079675	-0.056589	-0.137356	-0.137413	0.473655	0.126134	0.115327	0.031130	0.124085	-0.417771	0.082659	0.271551	-0.038523	0.100967	0.049068	0.029045	0.050625	0.083471	-0.136413	0.031232	0.013372	-0.013735	0.015090	0.031020	-0.030885	0.019322	-0.295310	-0.154093	-0.012382	-0.010212	0.004736	-0.018770	-0.455884	-0.188874	-0.271276	-0.002849	-0.000810	-0.169685	-0.172205	0.292178	-0.228479	-0.234008	-0.231594	-0.077005	-0.266873	0.092877
month	0.010205	0.007585	1.000000	0.005485	0.001372	-0.010645	0.000188	-0.015902	-0.002390	0.002428	-0.007522	0.001693	0.001159	0.000266	-0.012201	-0.018943	-0.003890	-0.005410	0.007523	0.009717	-0.041601	-0.024191	-0.001157	-0.002147	-0.009960	0.028223	0.025504	-0.006758	0.039036	0.027673	0.008852	0.005227	0.037771	0.035673	-0.006712	-0.045888	-0.084985	0.016581	0.035928	0.026994	0.024605	-0.096113	0.017931	0.029200	-0.082247	0.133230	0.009847	0.004606	0.010822	0.013472	-0.045108	-0.128683	-0.059837	0.063582	0.004155	0.004285	0.005767	0.002004	0.001435	0.004519	-0.000112	-0.006267	-0.003645	0.009172	0.009363	0.009622	-0.026648	-0.004028	-0.073089	-0.001555	0.066517	0.003059	-0.026813	0.016438	-0.024800	-0.003852	-0.003547	-0.003416	-0.007753	-0.001512
day	0.007057	0.006978	0.005485	1.000000	-0.001327	0.004825	0.007371	0.001397	0.008675	-0.007800	-0.006319	0.011041	-0.004006	-0.002291	-0.001802	-0.004179	-0.003096	-0.005132	0.000833	-0.004886	0.012287	-0.144920	-0.000268	-0.002307	0.006993	0.016416	0.020875	-0.025942	-0.054502	-0.039834	-0.050251	0.004741	0.027022	0.023289	-0.001641	0.011782	-0.118670	-0.015820	0.005552	0.003392	0.000449	-0.042107	0.035093	0.131652	0.043476	0.031835	0.001501	0.008442	0.021216	-0.005620	0.036411	-0.030031	-0.001784	0.082344	-0.004749	-0.002144	-0.004134	0.000517	0.001890	0.001184	-0.004497	-0.001644	-0.001118	0.008659	-0.018251	-0.003203	-0.047535	0.007152	-0.001384	0.013596	0.019458	0.005159	0.002655	-0.001562	0.013693	-0.006595	-0.006918	-0.003545	-0.005982	0.009514
extended	0.088933	0.088923	0.001372	-0.001327	1.000000	-0.026851	0.032504	-0.019201	0.037165	0.054347	0.017177	-0.016506	0.000896	0.046728	0.008817	0.090376	-0.020751	0.065440	-0.033063	0.268167	0.006901	-0.021069	0.007149	0.013145	0.011975	-0.028854	-0.012712	0.041500	0.060268	0.061809	0.015257	0.096260	0.035453	0.105548	0.039100	-0.077274	-0.169880	-0.003213	0.017236	0.005034	0.053912	-0.207015	0.030452	-0.057406	-0.062390	0.061214	0.201390	-0.114555	0.150400	0.138510	0.084213	0.101688	0.117879	0.208251	0.017696	-0.001541	0.004419	-0.012349	-0.001891	-0.001899	-0.012908	0.029112	-0.001526	0.408247	0.015291	-0.003039	0.178986	0.055386	-0.179355	-0.005537	0.033104	-0.047390	0.016856	0.175605	-0.185049	0.050541	0.053485	0.030440	0.064947	-0.195264
country	-0.144918	-0.144894	-0.010645	0.004825	-0.026851	1.000000	0.150792	0.216451	0.002030	-0.084609	-0.016601	-0.003257	-0.040363	-0.044322	0.043077	0.054848	-0.026759	-0.055631	-0.050038	-0.020701	0.039805	0.091397	-0.023511	-0.021324	0.572763	-0.028416	-0.026888	0.484330	-0.017009	-0.015717	0.412045	0.043812	0.182534	0.223098	-0.016782	-0.017398	-0.282162	-0.002085	-0.069664	-0.012140	-0.128221	0.033483	-0.082198	0.157317	0.103578	0.002878	-0.021702	-0.040641	-0.008422	-0.023695	0.018337	0.046826	0.301899	0.294948	-0.016915	0.005565	-0.008828	-0.009452	0.006397	-0.024807	0.032665	-0.023932	0.038869	-0.019809	-0.010311	-0.003277	-0.058584	-0.030025	0.027569	0.009545	0.056563	-0.081457	-0.036869	0.017857	-0.047574	0.079423	0.077992	0.212349	0.168451	-0.051108
region	0.391129	0.391142	0.000188	0.007371	0.032504	0.150792	1.000000	0.341703	0.452172	-0.089509	0.075372	0.018375	-0.015700	0.012154	0.071668	0.066021	-0.000797	-0.020784	0.100482	-0.004438	0.103274	0.134865	0.029113	0.022808	0.143829	0.068391	0.064815	0.118185	0.122288	0.074671	0.139252	0.232674	0.464819	0.391049	-0.007518	-0.080812	-0.160004	-0.075079	0.223525	-0.000064	0.153652	0.092754	0.285420	0.057395	-0.184638	-0.066079	0.004494	0.093825	0.053868	0.048491	0.107404	0.143923	0.051591	0.012752	0.037647	-0.015470	0.022470	0.045948	-0.020898	-0.011936	-0.181102	-0.069495	-0.004394	-0.024141	0.003637	-0.032196	-0.246651	-0.120691	-0.062253	0.032361	0.078801	-0.053309	-0.081445	0.192969	-0.160487	-0.153248	-0.148840	0.048161	-0.113385	0.075053
latitude	0.172221	0.172276	-0.015902	0.001397	-0.019201	0.216451	0.341703	1.000000	0.283142	-0.021387	0.009589	0.019179	-0.005304	-0.004557	0.067820	0.034444	-0.026256	-0.066895	0.067662	0.002602	-0.174193	-0.211265	-0.037395	-0.051064	0.031810	0.072788	0.074326	-0.151581	0.007107	-0.019372	-0.116506	0.042011	0.189383	0.117800	-0.061125	-0.057626	0.080799	-0.082155	-0.059984	0.030214	-0.052990	0.098388	-0.153730	0.177016	0.009961	-0.016673	-0.022132	0.104991	-0.031830	-0.058287	-0.098457	-0.131871	-0.170134	-0.142039	-0.023306	0.006760	-0.015072	0.020930	0.008200	0.012645	-0.086839	-0.052534	0.009192	-0.024914	0.019236	0.002625	-0.133086	-0.044167	-0.026167	0.060577	0.062228	-0.064826	0.034211	0.051753	0.006914	-0.140778	-0.139897	0.104096	-0.079731	0.075630
longitude	0.553091	0.553111	-0.002390	0.008675	0.037165	0.002030	0.452172	0.283142	1.000000	0.108872	0.061830	-0.005409	-0.008859	0.030443	0.108898	0.063357	-0.009770	-0.032825	0.074468	0.006102	-0.117136	-0.065659	0.034885	0.022581	0.087036	0.030164	0.021119	-0.013977	0.101083	0.098080	0.040811	0.111069	0.178879	0.035568	0.055580	0.094992	0.253205	-0.011990	0.284408	0.001034	0.098783	-0.015558	0.134339	-0.319233	-0.053968	-0.017399	-0.023382	0.017595	-0.008656	0.002351	0.004589	0.028538	0.093476	0.205996	0.001890	-0.018167	-0.004829	0.041285	-0.028958	0.013584	-0.177818	-0.073017	-0.010263	-0.015005	0.008687	-0.006635	-0.294441	-0.078447	-0.121560	0.004206	-0.025833	-0.128899	-0.139254	0.119757	-0.041735	-0.175436	-0.172980	-0.032613	-0.180382	0.037006
specificity	0.029533	0.029520	0.002428	-0.007800	0.054347	-0.084609	-0.089509	-0.021387	0.108872	1.000000	-0.038286	0.013099	0.000715	-0.077115	0.042615	-0.067663	0.040043	0.028813	-0.028427	0.049253	0.058724	0.119384	0.048390	0.045310	-0.031270	-0.017114	-0.010113	-0.019656	-0.017081	-0.043415	-0.078823	-0.023931	-0.139092	0.069391	-0.009817	-0.029859	-0.028731	-0.010806	0.038776	-0.009213	0.072283	-0.001656	0.014417	0.055860	-0.071928	-0.018794	0.057615	-0.089939	0.033731	0.045127	0.108759	0.157028	0.319712	0.202116	0.026006	-0.002262	0.027169	-0.023087	-0.004741	0.036543	0.023172	0.042295	-0.005309	0.020546	-0.000961	-0.005192	-0.023228	-0.003264	-0.001160	-0.016459	0.000937	-0.063543	-0.055242	0.030799	-0.031348	0.079861	0.079318	-0.022455	0.066226	-0.084506
vicinity	0.077430	0.077450	-0.007522	-0.006319	0.017177	-0.016601	0.075372	0.009589	0.061830	-0.038286	1.000000	0.003777	-0.000708	-0.049932	0.034685	-0.047956	-0.010752	0.002230	0.008907	0.003429	0.004126	-0.099688	0.028187	0.034531	0.008138	0.008148	0.015713	0.007248	0.009924	0.001864	-0.033637	0.036175	-0.028617	-0.162214	0.034319	0.009848	0.102272	-0.011768	0.016431	-0.008969	0.027086	-0.014499	0.032258	0.001554	-0.174236	-0.020791	-0.002007	-0.015398	0.008465	0.001927	-0.056377	-0.030279	0.001086	0.157410	0.016358	-0.001038	0.001699	-0.001243	-0.001062	0.001131	-0.028184	0.019185	-0.002421	0.004230	-0.000417	-0.037674	-0.039454	0.001488	-0.064515	0.002251	-0.028727	-0.035756	-0.031980	0.034029	-0.012263	0.003202	0.002500	-0.011080	-0.002772	-0.013761
crit1	0.000168	0.000140	0.001693	0.011041	-0.016506	-0.003257	0.018375	0.019179	-0.005409	0.013099	0.003777	1.000000	-0.009326	-0.039575	-0.059548	-0.303340	0.028852	-0.009134	0.017525	0.026369	-0.005979	-0.052006	-0.049232	-0.044915	-0.005581	-0.011470	-0.013061	-0.009095	-0.046491	-0.050018	-0.014507	0.029881	0.016858	NaN	0.022021	0.032245	NaN	-0.003517	-0.007606	0.003542	-0.001060	0.010062	NaN	NaN	NaN	-0.022175	0.029006	0.080171	-0.044347	-0.027395	-0.084488	-0.091579	NaN	NaN	0.010241	0.000114	0.007394	0.010642	-0.004274	0.004257	-0.004276	0.002559	0.001178	-0.017463	0.008070	0.014340	-0.022330	-0.018802	0.022699	0.018921	0.018996	0.004353	-0.091254	0.004447	0.004999	0.087052	0.086884	0.018515	0.078969	0.078870
crit2	0.024546	0.024544	0.001159	-0.004006	0.000896	-0.040363	-0.015700	-0.005304	-0.008859	0.000715	-0.000708	-0.009326	1.000000	-0.031249	-0.053470	-0.540929	0.016103	-0.011535	0.005613	0.003940	0.036633	0.030806	-0.107987	-0.115986	-0.021346	-0.027517	-0.035334	-0.013144	-0.035282	-0.044966	-0.011017	-0.015064	-0.031669	-0.111533	-0.003017	-0.043977	0.047314	-0.003058	0.019483	0.006186	-0.011346	0.007194	-0.075566	0.074908	NaN	-0.031557	-0.011272	0.041038	-0.020397	-0.019393	-0.025593	0.028285	NaN	NaN	-0.019597	0.000426	-0.006555	-0.003552	0.000798	-0.004359	-0.003320	0.004619	0.000621	0.002831	0.002222	0.009249	0.001691	0.003642	-0.005447	0.003016	NaN	NaN	NaN	-0.026565	0.022271	-0.027124	-0.027153	-0.013349	-0.031262	0.045892
crit3	0.008004	0.008009	0.000266	-0.002291	0.046728	-0.044322	0.012154	-0.004557	0.030443	-0.077115	-0.049932	-0.039575	-0.031249	1.000000	-0.226912	0.739957	0.056388	-0.032080	-0.008016	0.047378	0.176784	-0.024633	0.235869	0.185802	-0.082148	0.147569	0.136439	-0.042145	0.030516	0.013252	-0.004937	-0.059726	-0.095221	-0.073813	0.058317	0.162839	0.105692	0.014191	-0.035325	-0.036385	-0.016440	-0.013686	0.048715	0.090295	-0.092009	-0.066242	0.040741	0.173396	0.157582	0.226294	0.165484	0.210654	0.170994	0.123553	-0.057187	-0.001147	-0.077550	0.010291	-0.002894	-0.051963	0.006517	-0.236984	0.003700	0.042533	0.000436	-0.011756	0.028139	-0.011841	-0.040872	0.011447	0.007025	0.011612	0.007773	-0.014695	0.034898	-0.106573	-0.108488	-0.039076	-0.102216	0.081730
doubtterr	0.278633	0.278671	-0.012201	-0.001802	0.008817	0.043077	0.071668	0.067820	0.108898	0.042615	0.034685	-0.059548	-0.053470	-0.226912	1.000000	-0.013527	0.046117	-0.017044	0.053938	-0.086716	-0.144816	0.080444	0.007041	0.011418	0.040545	-0.032671	-0.021859	0.009594	-0.085934	-0.164225	0.029566	0.127896	0.100612	0.044902	0.071720	-0.029036	-0.022585	-0.161389	0.001835	-0.002060	0.019261	0.014129	-0.012369	-0.089146	0.172961	-0.041462	-0.151639	0.004867	-0.078502	-0.099443	-0.056007	-0.064521	-0.107028	-0.107455	0.044889	-0.000477	0.033645	0.032348	-0.002330	0.021962	-0.114088	0.006783	0.000006	-0.034154	0.013113	-0.028729	-0.165866	-0.101788	-0.059695	0.013378	-0.026711	-0.174019	-0.344508	0.175146	-0.159293	0.226361	0.227273	0.024566	0.160755	0.119270
alternative	0.063645	0.063702	-0.018943	-0.004179	0.090376	0.054848	0.066021	0.034444	0.063357	-0.067663	-0.047956	-0.303340	-0.540929	0.739957	-0.013527	1.000000	0.012323	-0.006387	-0.044468	0.038666	0.200516	0.102229	0.663002	0.622644	0.005311	0.364549	0.374790	-0.004099	0.122672	0.126293	0.164429	0.025992	0.002119	0.215992	0.055285	0.152997	-0.183591	0.029045	-0.089070	-0.053479	-0.006787	-0.040721	0.125120	-0.119474	0.266711	-0.036115	0.036519	0.066719	0.221905	0.253805	0.132587	0.189827	NaN	NaN	-0.023963	-0.010082	-0.051241	0.031702	-0.003768	-0.030390	-0.024681	-0.234248	-0.007999	0.060922	-0.017075	-0.009922	-0.028058	-0.029148	-0.104551	0.052100	0.047332	0.042566	0.054793	0.007434	0.010615	-0.179344	-0.180215	-0.040265	-0.158324	-0.051231
multiple	0.091466	0.091481	-0.003890	-0.003096	-0.020751	-0.026759	-0.000797	-0.026256	-0.009770	0.040043	-0.010752	0.028852	0.016103	0.056388	0.046117	0.012323	1.000000	0.030175	0.021614	0.075634	0.147922	0.127878	0.081633	0.080727	-0.019372	0.032571	0.050239	-0.003459	-0.048428	-0.006385	-0.054100	0.153984	0.029740	0.147596	0.029262	0.004880	-0.022006	0.024735	-0.017832	0.043731	-0.044885	-0.093515	-0.082531	0.062069	-0.022895	0.032108	0.022981	0.147349	0.055824	0.036935	0.120779	0.204377	NaN	NaN	-0.006079	0.011093	0.009983	0.007578	-0.003726	0.003995	-0.019647	-0.094793	-0.006405	-0.048177	-0.009945	-0.015355	-0.076560	-0.094735	0.011152	-0.003295	0.018828	-0.022776	-0.023865	0.116238	-0.114883	0.082953	0.081887	-0.028053	0.048626	0.115780
success	-0.049866	-0.049852	-0.005410	-0.005132	0.065440	-0.055631	-0.020784	-0.066895	-0.032825	0.028813	0.002230	-0.009134	-0.011535	-0.032080	-0.017044	-0.006387	0.030175	1.000000	-0.006434	0.066561	0.059887	0.104275	-0.030815	0.036035	-0.022026	0.046694	0.034705	-0.025810	0.039460	0.041297	-0.081567	0.014561	0.005358	0.162863	0.014203	0.017967	-0.062455	0.009513	0.010650	-0.005745	0.015962	0.014767	-0.148153	-0.120669	-0.182044	0.014095	0.012630	-0.073039	0.051292	0.053878	0.077341	0.083046	-0.040285	0.104743	0.056233	0.002938	0.003940	0.036288	0.004013	-0.000948	-0.035800	0.035571	0.002589	0.041714	0.019527	0.002748	0.009837	-0.005610	-0.024352	0.016280	0.004046	0.006693	0.004476	0.016109	-0.032208	0.059611	0.060016	-0.017657	0.035563	-0.032827
suicide	0.134558	0.134543	0.007523	0.000833	-0.033063	-0.050038	0.100482	0.067662	0.074468	-0.028427	0.008907	0.017525	0.005613	-0.008016	0.053938	-0.044468	0.021614	-0.006434	1.000000	-0.023721	-0.182289	-0.371633	-0.029985	-0.029194	-0.004592	0.064950	0.058674	-0.046214	0.009818	-0.016933	0.006150	0.123739	0.100696	0.170012	0.007541	-0.046951	0.040943	0.046471	0.025988	0.016142	0.023386	0.120798	0.064219	0.144746	-0.111736	-0.006484	-0.033066	0.090131	-0.052084	-0.075490	-0.262040	-0.328965	-0.199863	-0.205059	0.128684	0.024739	0.076607	0.144817	0.004953	0.006940	-0.069593	-0.006937	0.000912	-0.021783	0.009546	0.028492	-0.024855	-0.018526	-0.030694	-0.003016	NaN	NaN	NaN	0.030142	0.005994	-0.000401	0.001115	0.000310	-0.004842	0.160291
attacktype1	0.048222	0.048197	0.009717	-0.004886	0.268167	-0.020701	-0.004438	0.002602	0.006102	0.049253	0.003429	0.026369	0.003940	0.047378	-0.086716	0.038666	0.075634	0.066561	-0.023721	1.000000	-0.349480	-0.128349	0.012095	0.014070	0.022989	-0.015456	0.011920	0.037567	0.163660	0.210966	-0.003206	0.091261	0.000375	0.222508	0.021143	-0.123075	-0.154184	0.023535	-0.068530	0.016272	-0.008032	-0.059518	-0.042249	0.050096	-0.038157	0.104117	0.635562	0.346553	0.259289	0.210417	0.097483	0.068918	0.075942	0.032072	-0.006466	0.001392	0.024695	0.001256	0.007442	0.021275	0.030083	-0.038341	0.000182	0.203643	-0.009077	0.031370	-0.044504	0.049133	-0.083135	-0.008845	-0.028868	0.012730	0.020887	-0.003855	-0.047535	0.038970	0.039385	0.013573	0.055426	-0.096994
attacktype2	0.030693	0.030941	-0.041601	0.012287	0.006901	0.039805	0.103274	-0.174193	-0.117136	0.058724	0.004126	-0.005979	0.036633	0.176784	-0.144816	0.200516	0.147922	0.059887	-0.182289	-0.349480	1.000000	-0.293678	0.183779	0.182043	0.018146	0.066092	0.060952	-0.011270	-0.021404	0.029226	-0.145868	0.076569	-0.215551	-0.179799	0.008113	0.256262	0.745222	0.021186	0.006079	-0.004801	0.061960	-0.054759	0.132830	0.301344	0.041050	-0.013561	0.038328	-0.065636	0.550993	0.544632	0.523738	0.507777	NaN	NaN	-0.016305	-0.016709	-0.042043	-0.043402	-0.012683	-0.023409	0.173458	-0.095705	-0.016800	-0.061719	-0.022732	-0.010944	0.039790	-0.101793	0.041164	-0.095920	-0.086699	-0.061284	-0.094529	0.051984	-0.103588	0.021847	0.018162	-0.002880	0.015036	-0.335019
attacktype3	0.054855	0.055001	-0.024191	-0.144920	-0.021069	0.091397	0.134865	-0.211265	-0.065659	0.119384	-0.099688	-0.052006	0.030806	-0.024633	0.080444	0.102229	0.127878	0.104275	-0.371633	-0.128349	-0.293678	1.000000	0.248086	0.248499	0.084265	0.167483	0.182765	0.107977	-0.075101	-0.172649	-0.185267	0.036971	-0.588656	NaN	-0.058066	0.527046	NaN	-0.035193	0.012965	-0.079411	-0.033673	0.125487	NaN	NaN	NaN	-0.389212	-0.040496	-0.027865	0.175875	0.247412	0.166503	0.233761	NaN	NaN	-0.068305	-0.116119	-0.206514	-0.185556	-0.092653	-0.037692	0.199808	-0.077737	0.057522	-0.115943	-0.103455	-0.002640	-0.126307	-0.108734	-0.007460	0.184620	NaN	-0.514984	NaN	0.123005	-0.069251	0.038360	0.030498	-0.048854	0.048233	0.257285
targtype1	0.068833	0.068837	-0.001157	-0.000268	0.007149	-0.023511	0.029113	-0.037395	0.034885	0.048390	0.028187	-0.049232	-0.107987	0.235869	0.007041	0.663002	0.081633	-0.030815	-0.029985	0.012095	0.183779	0.248086	1.000000	0.988108	-0.039336	0.044115	0.066154	0.039459	0.165333	0.214894	-0.001524	0.024520	0.030820	-0.035982	0.017122	0.086819	0.006801	-0.005149	0.034712	-0.022242	-0.015433	-0.011450	0.133378	-0.043734	0.186260	0.004264	0.023806	0.130471	0.176996	0.237582	0.271574	0.377811	0.428393	0.322794	0.015216	0.001812	-0.032744	0.019337	-0.008540	-0.029575	0.001769	-0.041387	0.001227	0.005485	0.007480	-0.000129	-0.059013	-0.069611	-0.037220	-0.023941	0.009375	-0.099305	0.008996	0.084794	-0.043899	-0.049931	-0.053241	-0.050825	-0.052555	0.106886
targsubtype1	0.047099	0.047106	-0.002147	-0.002307	0.013145	-0.021324	0.022808	-0.051064	0.022581	0.045310	0.034531	-0.044915	-0.115986	0.185802	0.011418	0.622644	0.080727	0.036035	-0.029194	0.014070	0.182043	0.248499	0.988108	1.000000	-0.027975	0.071852	0.095163	0.031445	0.201831	0.259938	-0.020392	0.035721	0.038190	0.006899	0.017227	0.092803	0.019578	0.003420	0.037225	-0.017946	-0.010827	-0.025166	0.097249	-0.102734	0.149559	0.003589	0.027587	0.104680	0.169748	0.218614	0.250616	0.363614	0.446850	0.373297	0.019939	0.001602	-0.029278	0.022530	-0.008585	-0.027563	0.007128	-0.041586	0.001375	0.009265	0.004724	0.006628	-0.043001	-0.056933	-0.033325	-0.027989	-0.008339	-0.115490	0.048495	0.063791	-0.032332	-0.032829	-0.035646	-0.037745	-0.038051	0.071305
natlty1	-0.023637	-0.023609	-0.009960	0.006993	0.011975	0.572763	0.143829	0.031810	0.087036	-0.031270	0.008138	-0.005581	-0.021346	-0.082148	0.040545	0.005311	-0.019372	-0.022026	-0.004592	0.022989	0.018146	0.084265	-0.039336	-0.027975	1.000000	-0.044697	-0.067363	0.388320	-0.076478	-0.079974	0.321793	0.086045	0.110048	0.205465	-0.008808	0.010560	-0.123018	-0.003013	-0.058844	-0.005640	-0.075408	0.051781	-0.024410	0.065461	-0.195006	0.019261	0.000233	-0.047155	-0.038856	-0.046020	-0.008032	-0.005100	0.250807	0.154244	-0.000274	0.009335	0.001491	-0.003532	0.013539	-0.022997	-0.000925	-0.003524	0.009278	-0.002329	-0.000835	-0.056288	-0.016808	0.030716	0.020451	-0.002072	0.036658	-0.026875	0.059576	-0.023236	0.016045	0.037240	0.048039	0.366017	0.155235	-0.039283
targtype2	0.096598	0.096492	0.028223	0.016416	-0.028854	-0.028416	0.068391	0.072788	0.030164	-0.017114	0.008148	-0.011470	-0.027517	0.147569	-0.032671	0.364549	0.032571	0.046694	0.064950	-0.015456	0.066092	0.167483	0.044115	0.071852	-0.044697	1.000000	0.978316	-0.055889	0.230227	0.245973	0.010719	0.043144	0.095055	0.282626	-0.040798	0.040280	0.229295	0.044554	0.023948	0.033778	-0.027003	0.011604	0.012046	-0.005475	-0.476222	0.082303	0.007388	0.066337	0.084008	0.014256	0.062915	0.033054	NaN	1.000000	-0.007089	-0.027157	-0.005432	0.035054	-0.027930	-0.014735	-0.000774	0.011713	-0.028064	-0.041132	-0.003820	-0.023846	-0.133346	-0.091747	-0.039248	0.057208	0.156398	-0.058656	0.055053	0.070537	-0.024379	-0.079060	-0.083701	-0.081103	-0.097963	0.073966
targsubtype2	0.113583	0.113482	0.025504	0.020875	-0.012712	-0.026888	0.064815	0.074326	0.021119	-0.010113	0.015713	-0.013061	-0.035334	0.136439	-0.021859	0.374790	0.050239	0.034705	0.058674	0.011920	0.060952	0.182765	0.066154	0.095163	-0.067363	0.978316	1.000000	-0.038300	0.259385	0.295744	-0.009926	0.068712	0.126663	0.346946	-0.039782	0.024983	0.193068	0.071081	0.024818	0.025471	-0.020542	0.003440	0.034832	0.033458	-0.407852	0.039602	0.018882	0.052401	0.104937	0.008880	0.035747	-0.017816	NaN	1.000000	-0.009597	-0.034227	0.002061	0.032581	-0.021414	-0.010772	-0.003682	0.016357	-0.024501	-0.024770	-0.023613	-0.021009	-0.105079	-0.069826	-0.059998	0.045949	0.169222	-0.059868	0.078618	0.077325	-0.047155	-0.082476	-0.086455	-0.087315	-0.105167	0.049974
natlty2	-0.104678	-0.104635	-0.006758	-0.025942	0.041500	0.484330	0.118185	-0.151581	-0.013977	-0.019656	0.007248	-0.009095	-0.013144	-0.042145	0.009594	-0.004099	-0.003459	-0.025810	-0.046214	0.037567	-0.011270	0.107977	0.039459	0.031445	0.388320	-0.055889	-0.038300	1.000000	-0.058904	-0.043137	0.293686	0.095391	0.092825	0.027298	0.008060	-0.123111	0.148688	-0.016099	-0.030554	-0.021387	-0.072079	0.065366	0.054786	0.022039	0.124060	-0.071795	0.005726	-0.050857	-0.059913	-0.068653	0.068442	-0.016056	NaN	1.000000	0.041266	0.012830	0.007165	0.015756	0.038051	-0.031553	0.000096	0.016268	0.010718	0.056172	0.180125	-0.122464	0.091881	0.027275	0.072456	0.010577	0.206126	-0.098631	0.028187	-0.002226	0.190987	0.016864	0.028535	0.259955	0.123931	-0.046021
targtype3	0.149229	0.149068	0.039036	-0.054502	0.060268	-0.017009	0.122288	0.007107	0.101083	-0.017081	0.009924	-0.046491	-0.035282	0.030516	-0.085934	0.122672	-0.048428	0.039460	0.009818	0.163660	-0.021404	-0.075101	0.165333	0.201831	-0.076478	0.230227	0.259385	-0.058904	1.000000	0.977296	-0.017319	0.131476	0.144800	-1.000000	-0.031716	0.012931	NaN	0.150810	-0.032176	0.047450	0.027535	0.032111	0.164530	-1.000000	NaN	0.193276	0.122788	-0.024191	0.096917	0.006922	-0.201033	-0.126127	NaN	NaN	-0.023734	-0.037223	0.003305	0.013806	-0.023541	-0.011480	0.005333	0.042694	0.080479	0.049714	-0.079211	0.062794	-0.062307	-0.132249	0.029520	-0.023638	0.110811	-0.318670	0.206612	0.005734	-0.135547	-0.077724	-0.076460	-0.030702	-0.100342	-0.058701
targsubtype3	0.081981	0.081870	0.027673	-0.039834	0.061809	-0.015717	0.074671	-0.019372	0.098080	-0.043415	0.001864	-0.050018	-0.044966	0.013252	-0.164225	0.126293	-0.006385	0.041297	-0.016933	0.210966	0.029226	-0.172649	0.214894	0.259938	-0.079974	0.245973	0.295744	-0.043137	0.977296	1.000000	-0.018881	0.106529	0.111665	-1.000000	-0.045728	-0.030178	NaN	0.224409	-0.041138	0.025942	0.030654	-0.007544	0.262663	-1.000000	NaN	0.162470	0.150608	-0.003814	0.161921	0.039024	-0.165151	-0.081339	NaN	NaN	-0.020584	-0.027356	-0.007027	0.008298	-0.007602	-0.020827	0.023997	0.021358	0.106142	0.063799	-0.077613	0.027765	-0.125548	-0.053020	0.032671	-0.086248	-0.084441	-0.346607	-0.015329	-0.028977	-0.112903	-0.086118	-0.083376	-0.010270	-0.098619	-0.087561
natlty3	0.004898	0.004864	0.008852	-0.050251	0.015257	0.412045	0.139252	-0.116506	0.040811	-0.078823	-0.033637	-0.014507	-0.011017	-0.004937	0.029566	0.164429	-0.054100	-0.081567	0.006150	-0.003206	-0.145868	-0.185267	-0.001524	-0.020392	0.321793	0.010719	-0.009926	0.293686	-0.017319	-0.018881	1.000000	0.080901	0.369071	1.000000	-0.020982	-0.056511	NaN	-0.055643	0.050581	-0.008463	-0.063708	0.191115	-0.338120	1.000000	NaN	0.066966	-0.014472	-0.032425	-0.080330	-0.054684	-0.062575	-0.128383	NaN	NaN	0.034671	0.026303	0.016328	0.039957	0.007361	-0.053740	0.016726	-0.103444	0.253977	0.027688	0.042047	0.061611	0.039921	-0.001498	0.045388	0.127413	0.252067	-0.056576	0.189192	-0.045297	0.177521	0.015443	0.029293	0.232052	0.124770	-0.007143
ingroup	0.320870	0.320863	0.005227	0.004741	0.096260	0.043812	0.232674	0.042011	0.111069	-0.023931	0.036175	0.029881	-0.015064	-0.059726	0.127896	0.025992	0.153984	0.014561	0.123739	0.091261	0.076569	0.036971	0.024520	0.035721	0.086045	0.043144	0.068712	0.095391	0.131476	0.106529	0.080901	1.000000	0.478171	0.349947	0.185118	-0.091973	-0.075026	-0.018354	0.036894	0.141366	-0.053082	0.035752	-0.002050	-0.209335	-0.064626	0.340859	0.066514	0.024485	0.065721	-0.005890	0.037569	0.055756	-0.104627	0.044352	0.078688	0.004772	0.075082	0.039895	0.005872	0.024914	-0.121825	-0.023552	-0.004267	0.025821	0.016568	0.000275	-0.228559	-0.031172	-0.112264	0.039506	0.104314	-0.078637	-0.065762	0.083488	-0.035787	0.222771	0.224276	-0.004328	0.184116	-0.020158
ingroup2	0.467095	0.466974	0.037771	0.027022	0.035453	0.182534	0.464819	0.189383	0.178879	-0.139092	-0.028617	0.016858	-0.031669	-0.095221	0.100612	0.002119	0.029740	0.005358	0.100696	0.000375	-0.215551	-0.588656	0.030820	0.038190	0.110048	0.095055	0.126663	0.092825	0.144800	0.111665	0.369071	0.478171	1.000000	0.331314	-0.139132	-0.169974	-0.293437	-0.149174	0.135209	0.100744	-0.001291	0.076086	-0.012635	-0.265547	-0.059992	0.265002	-0.029868	0.019202	-0.015308	-0.065153	0.111139	0.208587	-0.139345	-0.139345	0.058089	-0.032034	0.073937	-0.035092	-0.047597	-0.002113	-0.123882	-0.042810	-0.085256	0.047862	0.081962	0.020704	-0.435057	0.172821	-0.153966	0.164900	0.156901	0.235952	NaN	0.022582	0.102421	-0.027741	-0.030374	-0.047323	-0.032209	0.055303
ingroup3	0.492234	0.491812	0.035673	0.023289	0.105548	0.223098	0.391049	0.117800	0.035568	0.069391	-0.162214	NaN	-0.111533	-0.073813	0.044902	0.215992	0.147596	0.162863	0.170012	0.222508	-0.179799	NaN	-0.035982	0.006899	0.205465	0.282626	0.346946	0.027298	-1.000000	-1.000000	1.000000	0.349947	0.331314	1.000000	-0.279272	-0.188191	-0.194851	-0.102160	0.223593	0.088147	-0.083699	-0.167251	-0.107466	-0.111752	-0.103570	-0.002594	0.161565	0.088244	-0.329961	-0.172448	0.497717	0.263105	NaN	NaN	0.104304	-0.000120	0.163677	-0.055315	-0.089408	0.010265	-0.037520	-0.117844	NaN	0.170606	-0.183859	0.135660	-0.403404	-0.384977	-0.532924	NaN	NaN	NaN	NaN	-0.097317	-0.254746	-0.019648	-0.173232	0.036173	-0.187605	-0.049805
guncertain1	0.079657	0.079675	-0.006712	-0.001641	0.039100	-0.016782	-0.007518	-0.061125	0.055580	-0.009817	0.034319	0.022021	-0.003017	0.058317	0.071720	0.055285	0.029262	0.014203	0.007541	0.021143	0.008113	-0.058066	0.017122	0.017227	-0.008808	-0.040798	-0.039782	0.008060	-0.031716	-0.045728	-0.020982	0.185118	-0.139132	-0.279272	1.000000	0.800062	0.735188	-0.007412	-0.016562	-0.031043	0.037644	-0.087926	-0.005787	0.079995	0.038898	0.034435	0.012259	0.017803	0.033440	0.051013	0.045309	0.049731	-0.125784	0.014141	0.005297	-0.002595	-0.018185	0.016567	-0.001954	-0.011662	-0.042845	0.027472	-0.002859	0.016769	-0.001036	-0.001729	-0.144030	-0.063392	-0.154711	-0.013500	-0.035661	-0.052783	-0.040881	0.086771	-0.054215	0.257119	0.257382	-0.007005	0.223806	-0.002948
guncertain2	-0.056744	-0.056589	-0.045888	0.011782	-0.077274	-0.017398	-0.080812	-0.057626	0.094992	-0.029859	0.009848	0.032245	-0.043977	0.162839	-0.029036	0.152997	0.004880	0.017967	-0.046951	-0.123075	0.256262	0.527046	0.086819	0.092803	0.010560	0.040280	0.024983	-0.123111	0.012931	-0.030178	-0.056511	-0.091973	-0.169974	-0.188191	0.800062	1.000000	0.756098	-0.027706	-0.032406	-0.136141	-0.009632	-0.080729	-0.056263	0.155397	0.049494	-0.131084	-0.126414	0.047616	0.015739	0.074793	0.081763	0.227394	NaN	NaN	-0.041901	0.034905	-0.086811	0.070555	0.043672	-0.079523	0.032791	0.032339	-0.045622	-0.022257	-0.067089	0.070129	0.230507	-0.072873	0.037366	-0.176649	-0.184637	0.341210	NaN	-0.107781	-0.077857	0.006106	0.047856	-0.033818	0.033380	0.107528
guncertain3	-0.137790	-0.137356	-0.084985	-0.118670	-0.169880	-0.282162	-0.160004	0.080799	0.253205	-0.028731	0.102272	NaN	0.047314	0.105692	-0.022585	-0.183591	-0.022006	-0.062455	0.040943	-0.154184	0.745222	NaN	0.006801	0.019578	-0.123018	0.229295	0.193068	0.148688	NaN	NaN	NaN	-0.075026	-0.293437	-0.194851	0.735188	0.756098	1.000000	0.064386	0.181871	-0.208799	0.144130	0.049369	0.101229	0.088615	0.159528	0.033739	-0.145878	-0.102162	-0.076098	-0.005298	-0.064875	0.184243	NaN	NaN	-0.075485	0.022937	-0.169288	0.083150	-0.047167	-0.055065	0.042250	0.317718	NaN	-0.094421	-0.093553	0.104639	0.612372	NaN	0.108893	NaN	NaN	NaN	NaN	-0.143877	-0.002750	0.282090	0.242215	0.067628	0.231277	0.081894
nperps	-0.137350	-0.137413	0.016581	-0.015820	-0.003213	-0.002085	-0.075079	-0.082155	-0.011990	-0.010806	-0.011768	-0.003517	-0.003058	0.014191	-0.161389	0.029045	0.024735	0.009513	0.046471	0.023535	0.021186	-0.035193	-0.005149	0.003420	-0.003013	0.044554	0.071081	-0.016099	0.150810	0.224409	-0.055643	-0.018354	-0.149174	-0.102160	-0.007412	-0.027706	0.064386	1.000000	0.027360	-0.031339	-0.006220	-0.062536	0.014869	-0.074692	0.092978	-0.361492	0.019260	-0.185342	0.006899	0.030287	-0.023918	-0.023564	0.073432	-0.006520	0.025289	0.009824	0.133172	0.030652	0.010237	0.070829	0.032552	0.036956	0.012670	0.008845	0.038268	-0.003021	0.254488	0.043888	0.042772	0.020936	-0.008042	0.121543	0.082137	-0.120628	0.139419	0.028509	0.028253	0.004316	0.030257	-0.072612
nperpcap	0.473824	0.473655	0.035928	0.005552	0.017236	-0.069664	0.223525	-0.059984	0.284408	0.038776	0.016431	-0.007606	0.019483	-0.035325	0.001835	-0.089070	-0.017832	0.010650	0.025988	-0.068530	0.006079	0.012965	0.034712	0.037225	-0.058844	0.023948	0.024818	-0.030554	-0.032176	-0.041138	0.050581	0.036894	0.135209	0.223593	-0.016562	-0.032406	0.181871	0.027360	1.000000	0.010583	0.095807	-0.054463	0.070638	-0.141514	-0.120593	-0.145039	-0.031826	-0.082054	-0.062002	-0.018462	-0.003537	-0.020102	-0.250000	-0.274649	0.013548	0.000134	0.005724	0.014973	-0.029006	0.008187	0.001222	0.031363	0.000167	0.009844	0.018266	-0.000190	0.003760	-0.000057	-0.104247	-0.012861	0.011468	-0.027489	0.004295	0.000335	0.005645	-0.002897	-0.002522	0.044276	0.003724	0.011627
claimed	0.126256	0.126134	0.026994	0.003392	0.005034	-0.012140	-0.000064	0.030214	0.001034	-0.009213	-0.008969	0.003542	0.006186	-0.036385	-0.002060	-0.053479	0.043731	-0.005745	0.016142	0.016272	-0.004801	-0.079411	-0.022242	-0.017946	-0.005640	0.033778	0.025471	-0.021387	0.047450	0.025942	-0.008463	0.141366	0.100744	0.088147	-0.031043	-0.136141	-0.208799	-0.031339	0.010583	1.000000	-0.036765	0.340569	-0.039660	0.067208	0.211447	0.359383	0.016443	0.016494	-0.016587	-0.038717	-0.070318	-0.098168	-0.408248	-0.434258	0.005799	0.004465	0.018184	-0.002213	0.002739	0.013302	0.053385	-0.055575	0.009868	-0.003868	0.010535	0.048462	0.003536	0.075051	0.028058	0.035922	0.007314	0.004343	-0.308770	-0.076291	0.086461	0.123522	0.124145	0.014558	0.107060	0.001045
claimmode	0.115381	0.115327	0.024605	0.000449	0.053912	-0.128221	0.153652	-0.052990	0.098783	0.072283	0.027086	-0.001060	-0.011346	-0.016440	0.019261	-0.006787	-0.044885	0.015962	0.023386	-0.008032	0.061960	-0.033673	-0.015433	-0.010827	-0.075408	-0.027003	-0.020542	-0.072079	0.027535	0.030654	-0.063708	-0.053082	-0.001291	-0.083699	0.037644	-0.009632	0.144130	-0.006220	0.095807	-0.036765	1.000000	-0.037657	0.613332	0.051544	0.531943	0.096103	0.039418	-0.071736	-0.017531	-0.045139	-0.048788	-0.031712	NaN	NaN	0.008736	-0.004948	0.021354	-0.014883	-0.016321	0.012812	-0.053553	0.051160	-0.022592	0.027224	0.035125	-0.045991	-0.011807	-0.024617	-0.069962	-0.060852	0.185129	0.101824	-0.096632	0.010944	-0.011543	-0.011405	-0.012039	0.033224	-0.006942	-0.043560
claim2	0.030646	0.031130	-0.096113	-0.042107	-0.207015	0.033483	0.092754	0.098388	-0.015558	-0.001656	-0.014499	0.010062	0.007194	-0.013686	0.014129	-0.040721	-0.093515	0.014767	0.120798	-0.059518	-0.054759	0.125487	-0.011450	-0.025166	0.051781	0.011604	0.003440	0.065366	0.032111	-0.007544	0.191115	0.035752	0.076086	-0.167251	-0.087926	-0.080729	0.049369	-0.062536	-0.054463	0.340569	-0.037657	1.000000	0.011037	0.453864	0.211447	0.130928	0.015925	0.084472	0.037232	0.058616	-0.343863	-0.354962	NaN	NaN	-0.038518	-0.066458	0.008077	-0.013052	-0.202700	-0.009667	0.059927	-0.114489	0.070038	-0.077740	-0.068688	0.302579	-0.361799	0.015337	-0.082299	0.042649	0.621921	-0.091959	NaN	0.048072	0.040913	0.027318	-0.042944	0.035443	-0.021627	0.117717
claimmode2	0.124254	0.124085	0.017931	0.035093	0.030452	-0.082198	0.285420	-0.153730	0.134339	0.014417	0.032258	NaN	-0.075566	0.048715	-0.012369	0.125120	-0.082531	-0.148153	0.064219	-0.042249	0.132830	NaN	0.133378	0.097249	-0.024410	0.012046	0.034832	0.054786	0.164530	0.262663	-0.338120	-0.002050	-0.012635	-0.107466	-0.005787	-0.056263	0.101229	0.014869	0.070638	-0.039660	0.613332	0.011037	1.000000	0.086091	0.701110	-0.022186	0.060883	-0.002411	-0.066019	0.128597	0.014244	-0.065300	NaN	NaN	-0.001471	0.026208	-0.018671	0.122050	-0.034568	-0.014938	0.038599	-0.016453	0.057268	0.096052	0.009408	0.090598	-0.103252	0.160587	-0.186263	0.336117	NaN	1.000000	NaN	0.086917	-0.053700	-0.073112	-0.114261	0.031089	-0.075177	0.179775
claim3	-0.417761	-0.417771	0.029200	0.131652	-0.057406	0.157317	0.057395	0.177016	-0.319233	0.055860	0.001554	NaN	0.074908	0.090295	-0.089146	-0.119474	0.062069	-0.120669	0.144746	0.050096	0.301344	NaN	-0.043734	-0.102734	0.065461	-0.005475	0.033458	0.022039	-1.000000	-1.000000	1.000000	-0.209335	-0.265547	-0.111752	0.079995	0.155397	0.088615	-0.074692	-0.141514	0.067208	0.051544	0.453864	0.086091	1.000000	0.328153	-0.090762	-0.022821	-0.055974	0.219344	0.195711	-0.508001	-0.349513	NaN	NaN	0.022974	0.055290	-0.020181	0.024403	0.085030	-0.080619	-0.025274	-0.143638	0.218218	-0.024859	0.268400	0.279925	-0.408248	-0.471666	-0.040032	NaN	NaN	NaN	NaN	0.050594	-0.150436	0.012608	-0.164594	0.028146	-0.118858	0.149810
claimmode3	0.082421	0.082659	-0.082247	0.043476	-0.062390	0.103578	-0.184638	0.009961	-0.053968	-0.071928	-0.174236	NaN	NaN	-0.092009	0.172961	0.266711	-0.022895	-0.182044	-0.111736	-0.038157	0.041050	NaN	0.186260	0.149559	-0.195006	-0.476222	-0.407852	0.124060	NaN	NaN	NaN	-0.064626	-0.059992	-0.103570	0.038898	0.049494	0.159528	0.092978	-0.120593	0.211447	0.531943	0.211447	0.701110	0.328153	1.000000	0.207371	0.026199	-0.001890	0.100411	0.289964	NaN	-0.307148	NaN	NaN	-0.209486	-0.111287	-0.036101	-0.125854	0.055117	NaN	0.092586	0.257425	0.238305	-0.021174	0.763863	0.790569	NaN	NaN	0.500244	NaN	NaN	NaN	NaN	-0.694365	0.914652	-0.014631	-0.131805	-0.299284	-0.076082	0.135286
compclaim	0.272146	0.271551	0.133230	0.031835	0.061214	0.002878	-0.066079	-0.016673	-0.017399	-0.018794	-0.020791	-0.022175	-0.031557	-0.066242	-0.041462	-0.036115	0.032108	0.014095	-0.006484	0.104117	-0.013561	-0.389212	0.004264	0.003589	0.019261	0.082303	0.039602	-0.071795	0.193276	0.162470	0.066966	0.340859	0.265002	-0.002594	0.034435	-0.131084	0.033739	-0.361492	-0.145039	0.359383	0.096103	0.130928	-0.022186	-0.090762	0.207371	1.000000	0.135906	0.036618	0.036513	-0.031887	-0.112598	-0.162605	NaN	NaN	-0.008346	-0.013213	0.064220	-0.020635	-0.003921	0.010518	0.116075	0.106430	0.065203	0.029763	-0.042754	0.068736	-0.173703	0.128789	-0.119713	-0.077179	0.039705	-0.108367	-0.079969	-0.084546	0.089514	0.188011	0.193223	0.000661	0.172587	-0.066698
weaptype1	-0.038496	-0.038523	0.009847	0.001501	0.201390	-0.021702	0.004494	-0.022132	-0.023382	0.057615	-0.002007	0.029006	-0.011272	0.040741	-0.151639	0.036519	0.022981	0.012630	-0.033066	0.635562	0.038328	-0.040496	0.023806	0.027587	0.000233	0.007388	0.018882	0.005726	0.122788	0.150608	-0.014472	0.066514	-0.029868	0.161565	0.012259	-0.126414	-0.145878	0.019260	-0.031826	0.016443	0.039418	0.015925	0.060883	-0.022821	0.026199	0.135906	1.000000	0.764294	0.136071	-0.039754	0.027873	-0.080225	0.051026	0.134563	-0.002685	0.008670	0.030630	-0.022339	-0.007001	0.022020	0.018415	-0.009299	-0.000687	0.170645	0.002104	0.023116	-0.031667	0.004902	-0.064859	-0.010745	-0.030813	0.068307	0.120271	0.015935	-0.037182	0.035244	0.035285	-0.010050	0.049276	-0.169767
weapsubtype1	0.100979	0.100967	0.004606	0.008442	-0.114555	-0.040641	0.093825	0.104991	0.017595	-0.089939	-0.015398	0.080171	0.041038	0.173396	0.004867	0.066719	0.147349	-0.073039	0.090131	0.346553	-0.065636	-0.027865	0.130471	0.104680	-0.047155	0.066337	0.052401	-0.050857	-0.024191	-0.003814	-0.032425	0.024485	0.019202	0.088244	0.017803	0.047616	-0.102162	-0.185342	-0.082054	0.016494	-0.071736	0.084472	-0.002411	-0.055974	-0.001890	0.036618	0.764294	1.000000	0.000662	-0.143789	0.018060	-0.081560	0.015669	0.009337	-0.073846	0.000250	-0.037225	0.042654	0.003978	-0.033573	0.034626	-0.190115	0.004555	-0.074860	-0.003477	-0.014526	-0.217952	-0.069186	-0.006727	-0.014986	-0.028565	-0.058699	-0.011415	0.011093	0.027428	-0.124174	-0.123801	-0.001089	-0.114073	0.639508
weaptype2	0.049107	0.049068	0.010822	0.021216	0.150400	-0.008422	0.053868	-0.031830	-0.008656	0.033731	0.008465	-0.044347	-0.020397	0.157582	-0.078502	0.221905	0.055824	0.051292	-0.052084	0.259289	0.550993	0.175875	0.176996	0.169748	-0.038856	0.084008	0.104937	-0.059913	0.096917	0.161921	-0.080330	0.065721	-0.015308	-0.329961	0.033440	0.015739	-0.076098	0.006899	-0.062002	-0.016587	-0.017531	0.037232	-0.066019	0.219344	0.100411	0.036513	0.136071	0.000662	1.000000	0.903163	0.253090	0.275170	0.167487	0.272276	0.011464	0.009591	-0.013158	-0.070004	-0.004068	0.008380	0.109194	-0.056253	0.037600	0.079138	-0.082155	-0.005766	-0.228262	-0.075790	-0.061492	-0.196358	-0.137716	-0.218838	0.089119	0.084271	-0.136887	-0.061919	-0.064585	-0.025441	-0.058558	-0.243656
weapsubtype2	0.029094	0.029045	0.013472	-0.005620	0.138510	-0.023695	0.048491	-0.058287	0.002351	0.045127	0.001927	-0.027395	-0.019393	0.226294	-0.099443	0.253805	0.036935	0.053878	-0.075490	0.210417	0.544632	0.247412	0.237582	0.218614	-0.046020	0.014256	0.008880	-0.068653	0.006922	0.039024	-0.054684	-0.005890	-0.065153	-0.172448	0.051013	0.074793	-0.005298	0.030287	-0.018462	-0.038717	-0.045139	0.058616	0.128597	0.195711	0.289964	-0.031887	-0.039754	-0.143789	0.903163	1.000000	0.192457	0.179036	0.072122	0.177456	0.018625	0.021832	-0.031562	-0.035976	-0.006678	-0.008233	0.128866	-0.078993	-0.004913	0.074541	-0.038802	0.062505	-0.303772	-0.094137	-0.030584	-0.193138	-0.095537	-0.180625	-0.340305	-0.028129	-0.062037	-0.072355	-0.073811	0.005799	-0.060736	-0.163070
weaptype3	0.050598	0.050625	-0.045108	0.036411	0.084213	0.018337	0.107404	-0.098457	0.004589	0.108759	-0.056377	-0.084488	-0.025593	0.165484	-0.056007	0.132587	0.120779	0.077341	-0.262040	0.097483	0.523738	0.166503	0.271574	0.250616	-0.008032	0.062915	0.035747	0.068442	-0.201033	-0.165151	-0.062575	0.037569	0.111139	0.497717	0.045309	0.081763	-0.064875	-0.023918	-0.003537	-0.070318	-0.048788	-0.343863	0.014244	-0.508001	NaN	-0.112598	0.027873	0.018060	0.253090	0.192457	1.000000	0.941126	0.254817	0.414652	0.056752	0.046113	-0.118006	-0.081756	-0.021161	-0.049650	0.125519	-0.125583	-0.046500	-0.004733	-0.172456	0.141311	-0.180219	-0.079119	-0.171472	-0.301823	NaN	NaN	NaN	0.042162	-0.213369	-0.046920	-0.053467	-0.044871	-0.056121	-0.211704
weapsubtype3	0.083092	0.083471	-0.128683	-0.030031	0.101688	0.046826	0.143923	-0.131871	0.028538	0.157028	-0.030279	-0.091579	0.028285	0.210654	-0.064521	0.189827	0.204377	0.083046	-0.328965	0.068918	0.507777	0.233761	0.377811	0.363614	-0.005100	0.033054	-0.017816	-0.016056	-0.126127	-0.081339	-0.128383	0.055756	0.208587	0.263105	0.049731	0.227394	0.184243	-0.023564	-0.020102	-0.098168	-0.031712	-0.354962	-0.065300	-0.349513	-0.307148	-0.162605	-0.080225	-0.081560	0.275170	0.179036	0.941126	1.000000	0.447792	0.340049	0.060872	0.065182	-0.132825	-0.090776	-0.020998	-0.068162	0.140658	-0.193161	-0.058999	-0.050961	-0.081084	0.129332	-0.274250	-0.011079	-0.031722	-0.316735	NaN	NaN	NaN	0.036080	-0.162824	-0.066502	-0.075466	-0.063517	-0.055507	-0.256816
weaptype4	-0.136695	-0.136413	-0.059837	-0.001784	0.117879	0.301899	0.051591	-0.170134	0.093476	0.319712	0.001086	NaN	NaN	0.170994	-0.107028	NaN	NaN	-0.040285	-0.199863	0.075942	NaN	NaN	0.428393	0.446850	0.250807	NaN	NaN	NaN	NaN	NaN	NaN	-0.104627	-0.139345	NaN	-0.125784	NaN	NaN	0.073432	-0.250000	-0.408248	NaN	NaN	NaN	NaN	NaN	NaN	0.051026	0.015669	0.167487	0.072122	0.254817	0.447792	1.000000	0.837112	0.099483	-0.200000	-0.064649	0.062727	NaN	0.559017	0.150446	0.067111	-0.262032	-0.050379	-0.372503	-0.019672	-0.268720	0.000000	-0.044394	NaN	NaN	NaN	NaN	-0.483896	0.259311	-0.182583	-0.199934	-0.024523	-0.080742	-0.224095
weapsubtype4	0.031511	0.031232	0.063582	0.082344	0.208251	0.294948	0.012752	-0.142039	0.205996	0.202116	0.157410	NaN	NaN	0.123553	-0.107455	NaN	NaN	0.104743	-0.205059	0.032072	NaN	NaN	0.322794	0.373297	0.154244	1.000000	1.000000	1.000000	NaN	NaN	NaN	0.044352	-0.139345	NaN	0.014141	NaN	NaN	-0.006520	-0.274649	-0.434258	NaN	NaN	NaN	NaN	NaN	NaN	0.134563	0.009337	0.272276	0.177456	0.414652	0.340049	0.837112	1.000000	0.111635	-0.247953	-0.505800	0.145234	NaN	-0.088288	0.127067	0.018435	-0.400820	0.015220	0.004766	0.472953	-0.280119	0.989554	0.165554	0.904194	NaN	NaN	NaN	-0.519332	0.798374	-0.292512	-0.315711	-0.078956	-0.234329	-0.258205
nkill	0.013383	0.013372	0.004155	-0.004749	0.017696	-0.016915	0.037647	-0.023306	0.001890	0.026006	0.016358	0.010241	-0.019597	-0.057187	0.044889	-0.023963	-0.006079	0.056233	0.128684	-0.006466	-0.016305	-0.068305	0.015216	0.019939	-0.000274	-0.007089	-0.009597	0.041266	-0.023734	-0.020584	0.034671	0.078688	0.058089	0.104304	0.005297	-0.041901	-0.075485	0.025289	0.013548	0.005799	0.008736	-0.038518	-0.001471	0.022974	-0.209486	-0.008346	-0.002685	-0.073846	0.011464	0.018625	0.056752	0.060872	0.099483	0.111635	1.000000	0.546581	0.394787	0.270090	0.042583	0.139514	-0.015299	0.022818	0.021531	-0.005581	0.209368	0.089172	-0.009629	-0.038451	-0.000330	0.072828	0.011212	-0.019389	-0.031223	0.052130	0.024171	0.056675	0.057400	-0.016067	0.041136	-0.048800
nkillus	-0.013713	-0.013735	0.004285	-0.002144	-0.001541	0.005565	-0.015470	0.006760	-0.018167	-0.002262	-0.001038	0.000114	0.000426	-0.001147	-0.000477	-0.010082	0.011093	0.002938	0.024739	0.001392	-0.016709	-0.116119	0.001812	0.001602	0.009335	-0.027157	-0.034227	0.012830	-0.037223	-0.027356	0.026303	0.004772	-0.032034	-0.000120	-0.002595	0.034905	0.022937	0.009824	0.000134	0.004465	-0.004948	-0.066458	0.026208	0.055290	-0.111287	-0.013213	0.008670	0.000250	0.009591	0.021832	0.046113	0.065182	-0.200000	-0.247953	0.546581	1.000000	0.006031	0.090724	0.417992	-0.000363	0.004271	-0.036667	0.482252	0.009009	0.020789	0.149791	0.055680	0.042002	0.006968	0.132575	0.587997	-0.009771	-0.005019	-0.007804	0.010414	0.007321	0.008487	0.005080	0.010069	-0.004405
nkillter	0.015116	0.015090	0.005767	-0.004134	0.004419	-0.008828	0.022470	-0.015072	-0.004829	0.027169	0.001699	0.007394	-0.006555	-0.077550	0.033645	-0.051241	0.009983	0.003940	0.076607	0.024695	-0.042043	-0.206514	-0.032744	-0.029278	0.001491	-0.005432	0.002061	0.007165	0.003305	-0.007027	0.016328	0.075082	0.073937	0.163677	-0.018185	-0.086811	-0.169288	0.133172	0.005724	0.018184	0.021354	0.008077	-0.018671	-0.020181	-0.036101	0.064220	0.030630	-0.037225	-0.013158	-0.031562	-0.118006	-0.132825	-0.064649	-0.505800	0.394787	0.006031	1.000000	0.084275	0.001034	0.670075	-0.025451	0.031023	0.005118	-0.018776	0.078872	-0.008388	0.035709	-0.010761	0.023501	-0.010004	0.013775	-0.009871	-0.006406	0.018045	0.024787	0.068638	0.069164	-0.000300	0.060943	-0.045060
nwound	0.031025	0.031020	0.002004	0.000517	-0.012349	-0.009452	0.045948	0.020930	0.041285	-0.023087	-0.001243	0.010642	-0.003552	0.010291	0.032348	0.031702	0.007578	0.036288	0.144817	0.001256	-0.043402	-0.185556	0.019337	0.022530	-0.003532	0.035054	0.032581	0.015756	0.013806	0.008298	0.039957	0.039895	-0.035092	-0.055315	0.016567	0.070555	0.083150	0.030652	0.014973	-0.002213	-0.014883	-0.013052	0.122050	0.024403	-0.125854	-0.020635	-0.022339	0.042654	-0.070004	-0.035976	-0.081756	-0.090776	0.062727	0.145234	0.270090	0.090724	0.084275	1.000000	0.158989	0.073094	-0.022121	-0.006884	0.123356	-0.015903	0.130908	-0.024732	-0.013967	-0.025825	0.002139	0.000515	-0.000614	-0.020522	-0.015510	0.068471	0.114396	0.008661	0.008875	-0.005726	0.000682	0.066293
nwoundus	-0.030874	-0.030885	0.001435	0.001890	-0.001891	0.006397	-0.020898	0.008200	-0.028958	-0.004741	-0.001062	-0.004274	0.000798	-0.002894	-0.002330	-0.003768	-0.003726	0.004013	0.004953	0.007442	-0.012683	-0.092653	-0.008540	-0.008585	0.013539	-0.027930	-0.021414	0.038051	-0.023541	-0.007602	0.007361	0.005872	-0.047597	-0.089408	-0.001954	0.043672	-0.047167	0.010237	-0.029006	0.002739	-0.016321	-0.202700	-0.034568	0.085030	0.055117	-0.003921	-0.007001	0.003978	-0.004068	-0.006678	-0.021161	-0.020998	NaN	NaN	0.042583	0.417992	0.001034	0.158989	1.000000	0.000102	0.005157	-0.010727	0.475270	-0.000687	0.036401	-0.035810	0.059425	0.010151	0.009080	-0.007419	-0.003300	-0.005654	-0.002915	0.007789	-0.015583	0.004307	0.005233	0.009018	0.007871	0.000642
nwoundte	0.019343	0.019322	0.004519	0.001184	-0.001899	-0.024807	-0.011936	0.012645	0.013584	0.036543	0.001131	0.004257	-0.004359	-0.051963	0.021962	-0.030390	0.003995	-0.000948	0.006940	0.021275	-0.023409	-0.037692	-0.029575	-0.027563	-0.022997	-0.014735	-0.010772	-0.031553	-0.011480	-0.020827	-0.053740	0.024914	-0.002113	0.010265	-0.011662	-0.079523	-0.055065	0.070829	0.008187	0.013302	0.012812	-0.009667	-0.014938	-0.080619	NaN	0.010518	0.022020	-0.033573	0.008380	-0.008233	-0.049650	-0.068162	0.559017	-0.088288	0.139514	-0.000363	0.670075	0.073094	0.000102	1.000000	-0.012545	0.022346	-0.000718	-0.003469	0.001124	0.001014	-0.006992	0.003162	0.014967	0.004074	NaN	-0.007583	NaN	-0.015788	0.009103	0.040688	0.040377	-0.002851	0.035910	-0.041481
property	-0.295301	-0.295310	-0.000112	-0.004497	-0.012908	0.032665	-0.181102	-0.086839	-0.177818	0.023172	-0.028184	-0.004276	-0.003320	0.006517	-0.114088	-0.024681	-0.019647	-0.035800	-0.069593	0.030083	0.173458	0.199808	0.001769	0.007128	-0.000925	-0.000774	-0.003682	0.000096	0.005333	0.023997	0.016726	-0.121825	-0.123882	-0.037520	-0.042845	0.032791	0.042250	0.032552	0.001222	0.053385	-0.053553	0.059927	0.038599	-0.025274	0.092586	0.116075	0.018415	0.034626	0.109194	0.128866	0.125519	0.140658	0.150446	0.127067	-0.015299	0.004271	-0.025451	-0.022121	0.005157	-0.012545	1.000000	-0.340912	0.001578	0.010770	-0.009855	0.031639	0.137913	0.037260	0.253136	0.001930	-0.039224	0.008894	0.018621	-0.103426	0.029737	0.088721	0.087461	0.022138	0.102700	-0.023668
propextent	-0.154098	-0.154093	-0.006267	-0.001644	0.029112	-0.023932	-0.069495	-0.052534	-0.073017	0.042295	0.019185	0.002559	0.004619	-0.236984	0.006783	-0.234248	-0.094793	0.035571	-0.006937	-0.038341	-0.095705	-0.077737	-0.041387	-0.041586	-0.003524	0.011713	0.016357	0.016268	0.042694	0.021358	-0.103444	-0.023552	-0.042810	-0.117844	0.027472	0.032339	0.317718	0.036956	0.031363	-0.055575	0.051160	-0.114489	-0.016453	-0.143638	0.257425	0.106430	-0.009299	-0.190115	-0.056253	-0.078993	-0.125583	-0.193161	0.067111	0.018435	0.022818	-0.036667	0.031023	-0.006884	-0.010727	0.022346	-0.340912	1.000000	-0.050616	0.022928	0.036801	-0.073239	0.091508	0.032368	-0.101621	0.180008	0.426740	0.273895	0.073515	0.066029	0.052780	0.066315	0.065597	0.026329	0.072352	-0.098013
propvalue	-0.012391	-0.012382	-0.003645	-0.001118	-0.001526	0.038869	-0.004394	0.009192	-0.010263	-0.005309	-0.002421	0.001178	0.000621	0.003700	0.000006	-0.007999	-0.006405	0.002589	0.000912	0.000182	-0.016800	0.057522	0.001227	0.001375	0.009278	-0.028064	-0.024501	0.010718	0.080479	0.106142	0.253977	-0.004267	-0.085256	NaN	-0.002859	-0.045622	NaN	0.012670	0.000167	0.009868	-0.022592	0.070038	0.057268	0.218218	0.238305	0.065203	-0.000687	0.004555	0.037600	-0.004913	-0.046500	-0.058999	-0.262032	-0.400820	0.021531	0.482252	0.005118	0.123356	0.475270	-0.000718	0.001578	-0.050616	1.000000	0.000252	-0.031451	0.054259	0.057416	0.043788	0.000860	-0.060054	-0.040999	-0.055536	-0.061455	-0.047883	0.031569	0.009594	0.007989	0.020905	0.008526	0.007640
ishostkid	-0.010183	-0.010212	0.009172	0.008659	0.408247	-0.019809	-0.024141	-0.024914	-0.015005	0.020546	0.004230	-0.017463	0.002831	0.042533	-0.034154	0.060922	-0.048177	0.041714	-0.021783	0.203643	-0.061719	-0.115943	0.005485	0.009265	-0.002329	-0.041132	-0.024770	0.056172	0.049714	0.063799	0.027688	0.025821	0.047862	0.170606	0.016769	-0.022257	-0.094421	0.008845	0.009844	-0.003868	0.027224	-0.077740	0.096052	-0.024859	-0.021174	0.029763	0.170645	-0.074860	0.079138	0.074541	-0.004733	-0.050961	-0.050379	0.015220	-0.005581	0.009009	-0.018776	-0.015903	-0.000687	-0.003469	0.010770	0.022928	0.000252	1.000000	0.000296	-0.000934	0.003414	0.010503	-0.173683	0.011636	NaN	-0.044826	NaN	0.033230	-0.036889	0.034188	0.035626	0.018356	0.045067	-0.143861
nhostkid	0.004759	0.004736	0.009363	-0.018251	0.015291	-0.010311	0.003637	0.019236	0.008687	-0.000961	-0.000417	0.008070	0.002222	0.000436	0.013113	-0.017075	-0.009945	0.019527	0.009546	-0.009077	-0.022732	-0.103455	0.007480	0.004724	-0.000835	-0.003820	-0.023613	0.180125	-0.079211	-0.077613	0.042047	0.016568	0.081962	-0.183859	-0.001036	-0.067089	-0.093553	0.038268	0.018266	0.010535	0.035125	-0.068688	0.009408	0.268400	0.763863	-0.042754	0.002104	-0.003477	-0.082155	-0.038802	-0.172456	-0.081084	-0.372503	0.004766	0.209368	0.020789	0.078872	0.130908	0.036401	0.001124	-0.009855	0.036801	-0.031451	0.000296	1.000000	-0.005907	0.003023	0.006653	0.004136	0.060645	-0.009913	-0.007589	-0.020417	0.021398	0.354204	0.008229	0.008172	0.001140	0.005139	-0.014304
nhostkidus	-0.018742	-0.018770	0.009622	-0.003203	-0.003039	-0.003277	-0.032196	0.002625	-0.006635	-0.005192	-0.037674	0.014340	0.009249	-0.011756	-0.028729	-0.009922	-0.015355	0.002748	0.028492	0.031370	-0.010944	-0.002640	-0.000129	0.006628	-0.056288	-0.023846	-0.021009	-0.122464	0.062794	0.027765	0.061611	0.000275	0.020704	0.135660	-0.001729	0.070129	0.104639	-0.003021	-0.000190	0.048462	-0.045991	0.302579	0.090598	0.279925	0.790569	0.068736	0.023116	-0.014526	-0.005766	0.062505	0.141311	0.129332	-0.019672	0.472953	0.089172	0.149791	-0.008388	-0.024732	-0.035810	0.001014	0.031639	-0.073239	0.054259	-0.000934	-0.005907	1.000000	0.018845	0.031802	0.039021	0.007793	0.005160	0.013008	0.022869	-0.028739	-0.028511	0.003413	0.003058	-0.026020	-0.014519	-0.005646
nhours	-0.455918	-0.455884	-0.026648	-0.047535	0.178986	-0.058584	-0.246651	-0.133086	-0.294441	-0.023228	-0.039454	-0.022330	0.001691	0.028139	-0.165866	-0.028058	-0.076560	0.009837	-0.024855	-0.044504	0.039790	-0.126307	-0.059013	-0.043001	-0.016808	-0.133346	-0.105079	0.091881	-0.062307	-0.125548	0.039921	-0.228559	-0.435057	-0.403404	-0.144030	0.230507	0.612372	0.254488	0.003760	0.003536	-0.011807	-0.361799	-0.103252	-0.408248	NaN	-0.173703	-0.031667	-0.217952	-0.228262	-0.303772	-0.180219	-0.274250	-0.268720	-0.280119	-0.009629	0.055680	0.035709	-0.013967	0.059425	-0.006992	0.137913	0.091508	0.057416	0.003414	0.003023	0.018845	1.000000	0.195118	0.181191	0.094008	-0.089520	0.121525	0.277595	-0.301248	0.184045	0.062626	0.066565	0.072557	0.097766	0.002260
ndays	-0.188772	-0.188874	-0.004028	0.007152	0.055386	-0.030025	-0.120691	-0.044167	-0.078447	-0.003264	0.001488	-0.018802	0.003642	-0.011841	-0.101788	-0.029148	-0.094735	-0.005610	-0.018526	0.049133	-0.101793	-0.108734	-0.069611	-0.056933	0.030716	-0.091747	-0.069826	0.027275	-0.132249	-0.053020	-0.001498	-0.031172	0.172821	-0.384977	-0.063392	-0.072873	NaN	0.043888	-0.000057	0.075051	-0.024617	0.015337	0.160587	-0.471666	NaN	0.128789	0.004902	-0.069186	-0.075790	-0.094137	-0.079119	-0.011079	0.000000	0.989554	-0.038451	0.042002	-0.010761	-0.025825	0.010151	0.003162	0.037260	0.032368	0.043788	0.010503	0.006653	0.031802	0.195118	1.000000	0.077967	0.132974	0.012201	0.055480	0.013097	-0.493438	0.413987	0.079917	0.090344	0.169558	0.134139	-0.020015
ransom	-0.271519	-0.271276	-0.073089	-0.001384	-0.179355	0.027569	-0.062253	-0.026167	-0.121560	-0.001160	-0.064515	0.022699	-0.005447	-0.040872	-0.059695	-0.104551	0.011152	-0.024352	-0.030694	-0.083135	0.041164	-0.007460	-0.037220	-0.033325	0.020451	-0.039248	-0.059998	0.072456	0.029520	0.032671	0.045388	-0.112264	-0.153966	-0.532924	-0.154711	0.037366	0.108893	0.042772	-0.104247	0.028058	-0.069962	-0.082299	-0.186263	-0.040032	0.500244	-0.119713	-0.064859	-0.006727	-0.061492	-0.030584	-0.171472	-0.031722	-0.044394	0.165554	-0.000330	0.006968	0.023501	0.002139	0.009080	0.014967	0.253136	-0.101621	0.000860	-0.173683	0.004136	0.039021	0.181191	0.077967	1.000000	0.021865	0.020894	0.049178	0.009317	-0.171098	0.125086	0.010130	0.010482	0.008409	0.019062	0.044969
ransomamt	-0.002849	-0.002849	-0.001555	0.013596	-0.005537	0.009545	0.032361	0.060577	0.004206	-0.016459	0.002251	0.018921	0.003016	0.011447	0.013378	0.052100	-0.003295	0.016280	-0.003016	-0.008845	-0.095920	0.184620	-0.023941	-0.027989	-0.002072	0.057208	0.045949	0.010577	-0.023638	-0.086248	0.127413	0.039506	0.164900	NaN	-0.013500	-0.176649	NaN	0.020936	-0.012861	0.035922	-0.060852	0.042649	0.336117	NaN	NaN	-0.077179	-0.010745	-0.014986	-0.196358	-0.193138	-0.301823	-0.316735	NaN	0.904194	0.072828	0.132575	-0.010004	0.000515	-0.007419	0.004074	0.001930	0.180008	-0.060054	0.011636	0.060645	0.007793	0.094008	0.132974	0.021865	1.000000	0.366621	0.089996	-0.011159	-0.003207	0.053834	0.016886	0.017811	0.019470	0.027997	-0.006941
ransomamtus	-0.000461	-0.000810	0.066517	0.019458	0.033104	0.056563	0.078801	0.062228	-0.025833	0.000937	-0.028727	0.018996	NaN	0.007025	-0.026711	0.047332	0.018828	0.004046	NaN	-0.028868	-0.086699	NaN	0.009375	-0.008339	0.036658	0.156398	0.169222	0.206126	0.110811	-0.084441	0.252067	0.104314	0.156901	NaN	-0.035661	-0.184637	NaN	-0.008042	0.011468	0.007314	0.185129	0.621921	NaN	NaN	NaN	0.039705	-0.030813	-0.028565	-0.137716	-0.095537	NaN	NaN	NaN	NaN	0.011212	0.587997	0.013775	-0.000614	-0.003300	NaN	-0.039224	0.426740	-0.040999	NaN	-0.009913	0.005160	-0.089520	0.012201	0.020894	0.366621	1.000000	0.008500	-0.000285	0.053442	0.034827	0.022348	0.032029	0.121914	0.053744	-0.005729
ransompaid	-0.169650	-0.169685	0.003059	0.005159	-0.047390	-0.081457	-0.053309	-0.064826	-0.128899	-0.063543	-0.035756	0.004353	NaN	0.011612	-0.174019	0.042566	-0.022776	0.006693	NaN	0.012730	-0.061284	-0.514984	-0.099305	-0.115490	-0.026875	-0.058656	-0.059868	-0.098631	-0.318670	-0.346607	-0.056576	-0.078637	0.235952	NaN	-0.052783	0.341210	NaN	0.121543	-0.027489	0.004343	0.101824	-0.091959	1.000000	NaN	NaN	-0.108367	0.068307	-0.058699	-0.218838	-0.180625	NaN	NaN	NaN	NaN	-0.019389	-0.009771	-0.009871	-0.020522	-0.005654	-0.007583	0.008894	0.273895	-0.055536	-0.044826	-0.007589	0.013008	0.121525	0.055480	0.049178	0.089996	0.008500	1.000000	-0.002433	-0.104193	0.066349	-0.082402	-0.079871	0.001113	-0.082577	-0.009473
ransompaidus	-0.172339	-0.172205	-0.026813	0.002655	0.016856	-0.036869	-0.081445	0.034211	-0.139254	-0.055242	-0.031980	-0.091254	NaN	0.007773	-0.344508	0.054793	-0.023865	0.004476	NaN	0.020887	-0.094529	NaN	0.008996	0.048495	0.059576	0.055053	0.078618	0.028187	0.206612	-0.015329	0.189192	-0.065762	NaN	NaN	-0.040881	NaN	NaN	0.082137	0.004295	-0.308770	-0.096632	NaN	NaN	NaN	NaN	-0.079969	0.120271	-0.011415	0.089119	-0.340305	NaN	NaN	NaN	NaN	-0.031223	-0.005019	-0.006406	-0.015510	-0.002915	NaN	0.018621	0.073515	-0.061455	NaN	-0.020417	0.022869	0.277595	0.013097	0.009317	-0.011159	-0.000285	-0.002433	1.000000	-0.076922	0.034879	-0.049916	-0.044482	0.138033	0.062671	-0.006338
hostkidoutcome	0.292161	0.292178	0.016438	-0.001562	0.175605	0.017857	0.192969	0.051753	0.119757	0.030799	0.034029	0.004447	-0.026565	-0.014695	0.175146	0.007434	0.116238	0.016109	0.030142	-0.003855	0.051984	0.123005	0.084794	0.063791	-0.023236	0.070537	0.077325	-0.002226	0.005734	-0.028977	-0.045297	0.083488	0.022582	-0.097317	0.086771	-0.107781	-0.143877	-0.120628	0.000335	-0.076291	0.010944	0.048072	0.086917	0.050594	-0.694365	-0.084546	0.015935	0.011093	0.084271	-0.028129	0.042162	0.036080	-0.483896	-0.519332	0.052130	-0.007804	0.018045	0.068471	0.007789	-0.015788	-0.103426	0.066029	-0.047883	0.033230	0.021398	-0.028739	-0.301248	-0.493438	-0.171098	-0.003207	0.053442	-0.104193	-0.076922	1.000000	-0.627725	-0.057010	-0.062335	-0.129516	-0.121607	-0.016443
nreleased	-0.228536	-0.228479	-0.024800	0.013693	-0.185049	-0.047574	-0.160487	0.006914	-0.041735	-0.031348	-0.012263	0.004999	0.022271	0.034898	-0.159293	0.010615	-0.114883	-0.032208	0.005994	-0.047535	-0.103588	-0.069251	-0.043899	-0.032332	0.016045	-0.024379	-0.047155	0.190987	-0.135547	-0.112903	0.177521	-0.035787	0.102421	-0.254746	-0.054215	-0.077857	-0.002750	0.139419	0.005645	0.086461	-0.011543	0.040913	-0.053700	-0.150436	0.914652	0.089514	-0.037182	0.027428	-0.136887	-0.062037	-0.213369	-0.162824	0.259311	0.798374	0.024171	0.010414	0.024787	0.114396	-0.015583	0.009103	0.029737	0.052780	0.031569	-0.036889	0.354204	-0.028511	0.184045	0.413987	0.125086	0.053834	0.034827	0.066349	0.034879	-0.627725	1.000000	0.056317	0.061052	0.098843	0.099866	-0.023140
INT_LOG	-0.234006	-0.234008	-0.003852	-0.006595	0.050541	0.079423	-0.153248	-0.140778	-0.175436	0.079861	0.003202	0.087052	-0.027124	-0.106573	0.226361	-0.179344	0.082953	0.059611	-0.000401	0.038970	0.021847	0.038360	-0.049931	-0.032829	0.037240	-0.079060	-0.082476	0.016864	-0.077724	-0.086118	0.015443	0.222771	-0.027741	-0.019648	0.257119	0.006106	0.282090	0.028509	-0.002897	0.123522	-0.011405	0.027318	-0.073112	0.012608	-0.014631	0.188011	0.035244	-0.124174	-0.061919	-0.072355	-0.046920	-0.066502	-0.182583	-0.292512	0.056675	0.007321	0.068638	0.008661	0.004307	0.040688	0.088721	0.066315	0.009594	0.034188	0.008229	0.003413	0.062626	0.079917	0.010130	0.016886	0.022348	-0.082402	-0.049916	-0.057010	0.056317	1.000000	0.996571	0.060332	0.880709	-0.111608
INT_IDEO	-0.231593	-0.231594	-0.003547	-0.006918	0.053485	0.077992	-0.148840	-0.139897	-0.172980	0.079318	0.002500	0.086884	-0.027153	-0.108488	0.227273	-0.180215	0.081887	0.060016	0.001115	0.039385	0.018162	0.030498	-0.053241	-0.035646	0.048039	-0.083701	-0.086455	0.028535	-0.076460	-0.083376	0.029293	0.224276	-0.030374	-0.173232	0.257382	0.047856	0.242215	0.028253	-0.002522	0.124145	-0.012039	-0.042944	-0.114261	-0.164594	-0.131805	0.193223	0.035285	-0.123801	-0.064585	-0.073811	-0.053467	-0.075466	-0.199934	-0.315711	0.057400	0.008487	0.069164	0.008875	0.005233	0.040377	0.087461	0.065597	0.007989	0.035626	0.008172	0.003058	0.066565	0.090344	0.010482	0.017811	0.032029	-0.079871	-0.044482	-0.062335	0.061052	0.996571	1.000000	0.091938	0.883116	-0.110908
INT_MISC	-0.077012	-0.077005	-0.003416	-0.003545	0.030440	0.212349	0.048161	0.104096	-0.032613	-0.022455	-0.011080	0.018515	-0.013349	-0.039076	0.024566	-0.040265	-0.028053	-0.017657	0.000310	0.013573	-0.002880	-0.048854	-0.050825	-0.037745	0.366017	-0.081103	-0.087315	0.259955	-0.030702	-0.010270	0.232052	-0.004328	-0.047323	0.036173	-0.007005	-0.033818	0.067628	0.004316	0.044276	0.014558	0.033224	0.035443	0.031089	0.028146	-0.299284	0.000661	-0.010050	-0.001089	-0.025441	0.005799	-0.044871	-0.063517	-0.024523	-0.078956	-0.016067	0.005080	-0.000300	-0.005726	0.009018	-0.002851	0.022138	0.026329	0.020905	0.018356	0.001140	-0.026020	0.072557	0.169558	0.008409	0.019470	0.121914	0.001113	0.138033	-0.129516	0.098843	0.060332	0.091938	1.000000	0.266785	-0.004481
INT_ANY	-0.266880	-0.266873	-0.007753	-0.005982	0.064947	0.168451	-0.113385	-0.079731	-0.180382	0.066226	-0.002772	0.078969	-0.031262	-0.102216	0.160755	-0.158324	0.048626	0.035563	-0.004842	0.055426	0.015036	0.048233	-0.052555	-0.038051	0.155235	-0.097963	-0.105167	0.123931	-0.100342	-0.098619	0.124770	0.184116	-0.032209	-0.187605	0.223806	0.033380	0.231277	0.030257	0.003724	0.107060	-0.006942	-0.021627	-0.075177	-0.118858	-0.076082	0.172587	0.049276	-0.114073	-0.058558	-0.060736	-0.056121	-0.055507	-0.080742	-0.234329	0.041136	0.010069	0.060943	0.000682	0.007871	0.035910	0.102700	0.072352	0.008526	0.045067	0.005139	-0.014519	0.097766	0.134139	0.019062	0.027997	0.053744	-0.082577	0.062671	-0.121607	0.099866	0.880709	0.883116	0.266785	1.000000	-0.114971
is_bombing	0.092875	0.092877	-0.001512	0.009514	-0.195264	-0.051108	0.075053	0.075630	0.037006	-0.084506	-0.013761	0.078870	0.045892	0.081730	0.119270	-0.051231	0.115780	-0.032827	0.160291	-0.096994	-0.335019	0.257285	0.106886	0.071305	-0.039283	0.073966	0.049974	-0.046021	-0.058701	-0.087561	-0.007143	-0.020158	0.055303	-0.049805	-0.002948	0.107528	0.081894	-0.072612	0.011627	0.001045	-0.043560	0.117717	0.179775	0.149810	0.135286	-0.066698	-0.169767	0.639508	-0.243656	-0.163070	-0.211704	-0.256816	-0.224095	-0.258205	-0.048800	-0.004405	-0.045060	0.066293	0.000642	-0.041481	-0.023668	-0.098013	0.007640	-0.143861	-0.014304	-0.005646	0.002260	-0.020015	0.044969	-0.006941	-0.005729	-0.009473	-0.006338	-0.016443	-0.023140	-0.111608	-0.110908	-0.004481	-0.114971	1.000000

Dep. Variable:	sum	No. Observations:	23
Model:	ARMA(2, 0)	Log Likelihood	-154.654
Method:	css-mle	S.D. of innovations	190.399
Date:	Wed, 10 May 2017	AIC	317.308
Time:	19:18:58	BIC	321.850
Sample:	12-31-1970	HQIC	318.450
	- 12-31-1992

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	1044.9193	589.356	1.773	0.091	-110.196	2200.035
ar.L1.sum	0.8015	0.211	3.798	0.001	0.388	1.215
ar.L2.sum	0.1643	0.218	0.752	0.461	-0.264	0.592

AR.1
	Real	Imaginary	Modulus	Frequency
AR.2

Dep. Variable:	sum	No. Observations:	22
Model:	ARMA(2, 0)	Log Likelihood	-179.408
Method:	css-mle	S.D. of innovations	795.479
Date:	Wed, 10 May 2017	AIC	366.815
Time:	19:30:37	BIC	371.179
Sample:	12-31-2015	HQIC	367.843
	- 12-31-1994

	sum
approxdate
2015-12-31	7615.0
2014-12-31	8759.0
2013-12-31	6669.0
2012-12-31	5080.0
2011-12-31	2587.0

	sum	change_in_numattacks
approxdate
1970-01-01	1	NaN
1970-01-02	1	0.0
1970-01-03	0	-1.0
1970-01-06	0	0.0
1970-01-08	0	0.0

	approxdate	is_bombing
0	1970-02-05	0
1	1970-09-06	0
2	1970-01-08	0
3	1970-01-14	1
4	1970-01-16	0

	sum	change_in_numattacks
approxdate
1970-01-31	15.0	0.0
1970-02-28	32.0	-1.0
1970-03-31	41.0	2.0
1970-04-30	46.0	-1.0
1970-05-31	28.0	0.0
1970-06-30	26.0	-1.0
1970-07-31	38.0	0.0
1970-08-31	20.0	1.0
1970-09-30	20.0	-1.0
1970-10-31	31.0	4.0
1970-11-30	20.0	-4.0
1970-12-31	16.0	1.0
1971-01-31	18.0	3.0
1971-02-28	23.0	-2.0
1971-03-31	28.0	-2.0
1971-04-30	33.0	1.0
1971-05-31	24.0	-1.0
1971-06-30	21.0	1.0
1971-07-31	17.0	0.0
1971-08-31	23.0	0.0
1971-09-30	16.0	-1.0
1971-10-31	10.0	0.0
1971-11-30	14.0	1.0
1971-12-31	11.0	0.0
1972-01-31	23.0	0.0
1972-02-29	14.0	-1.0
1972-03-31	16.0	1.0
1972-04-30	8.0	-1.0
1972-05-31	38.0	4.0
1972-06-30	10.0	-4.0
1972-07-31	13.0	1.0
1972-08-31	14.0	-1.0
1972-09-30	10.0	1.0
1972-10-31	12.0	0.0
1972-11-30	13.0	-1.0
1972-12-31	15.0	0.0
1973-01-31	10.0	0.0
1973-02-28	5.0	0.0
1973-03-31	18.0	1.0
1973-04-30	12.0	1.0
1973-05-31	19.0	0.0
1973-06-30	5.0	-2.0
1973-07-31	14.0	1.0
1973-08-31	13.0	0.0
1973-09-30	13.0	-1.0
1973-10-31	13.0	0.0
1973-11-30	12.0	0.0
1973-12-31	15.0	4.0
1974-01-31	13.0	-4.0
1974-02-28	26.0	1.0
1974-03-31	29.0	-1.0
1974-04-30	13.0	1.0
1974-05-31	27.0	1.0
1974-06-30	23.0	-2.0
1974-07-31	16.0	0.0
1974-08-31	18.0	1.0
1974-09-30	29.0	-1.0
1974-10-31	29.0	1.0
1974-11-30	22.0	-1.0
1974-12-31	39.0	7.0
1975-01-31	22.0	-7.0
1975-02-28	36.0	1.0
1975-03-31	47.0	2.0
1975-04-30	27.0	-1.0
1975-05-31	39.0	0.0
1975-06-30	20.0	-2.0
1975-07-31	16.0	1.0
1975-08-31	28.0	-1.0
1975-09-30	39.0	1.0
1975-10-31	45.0	1.0
...	...	...
2010-03-31	196.0	-4.0
2010-04-30	223.0	-1.0
2010-05-31	224.0	-1.0
2010-06-30	203.0	6.0
2010-07-31	220.0	-7.0
2010-08-31	209.0	0.0
2010-09-30	251.0	7.0
2010-10-31	177.0	-5.0
2010-11-30	257.0	4.0
2010-12-31	189.0	-4.0
2011-01-31	199.0	5.0
2011-02-28	192.0	-5.0
2011-03-31	218.0	5.0
2011-04-30	220.0	-9.0
2011-05-31	211.0	6.0
2011-06-30	175.0	2.0
2011-07-31	171.0	-4.0
2011-08-31	181.0	-3.0
2011-09-30	152.0	4.0
2011-10-31	180.0	0.0
2011-11-30	395.0	1.0
2011-12-31	293.0	-2.0
2012-01-31	437.0	4.0
2012-02-29	329.0	5.0
2012-03-31	384.0	4.0
2012-04-30	445.0	4.0
2012-05-31	543.0	-3.0
2012-06-30	463.0	-3.0
2012-07-31	421.0	9.0
2012-08-31	464.0	-10.0
2012-09-30	382.0	6.0
2012-10-31	448.0	-11.0
2012-11-30	438.0	2.0
2012-12-31	326.0	15.0
2013-01-31	452.0	-15.0
2013-02-28	390.0	9.0
2013-03-31	428.0	-7.0
2013-04-30	538.0	5.0
2013-05-31	618.0	-9.0
2013-06-30	467.0	13.0
2013-07-31	660.0	3.0
2013-08-31	576.0	-16.0
2013-09-30	509.0	14.0
2013-10-31	684.0	-4.0
2013-11-30	689.0	-4.0
2013-12-31	658.0	11.0
2014-01-31	704.0	-2.0
2014-02-28	774.0	3.0
2014-03-31	841.0	3.0
2014-04-30	743.0	-4.0
2014-05-31	926.0	-4.0
2014-06-30	669.0	4.0
2014-07-31	900.0	-5.0
2014-08-31	678.0	0.0
2014-09-30	606.0	1.0
2014-10-31	657.0	12.0
2014-11-30	663.0	-16.0
2014-12-31	598.0	8.0
2015-01-31	824.0	-5.0
2015-02-28	750.0	1.0
2015-03-31	639.0	-9.0
2015-04-30	603.0	10.0
2015-05-31	623.0	-1.0
2015-06-30	591.0	13.0
2015-07-31	659.0	-23.0
2015-08-31	637.0	5.0
2015-09-30	517.0	-1.0
2015-10-31	630.0	7.0
2015-11-30	572.0	-2.0
2015-12-31	570.0	-1.0

	sum	change
yearmonth
1970-01-01	15.0	NaN
1970-02-01	32.0	17.0
1970-03-01	41.0	9.0
1970-04-01	46.0	5.0
1970-05-01	28.0	-18.0

	sum	change	SAD	SADchange	predict
yearmonth
1994-01-01	125.0	-93.0	-6.0	-121.0	20.654502
1994-02-01	99.0	-26.0	-19.0	-13.0	27.145873
1994-03-01	140.0	41.0	-5.0	14.0	39.474851
1994-04-01	107.0	-33.0	4.0	9.0	39.660698
1994-05-01	98.0	-9.0	-56.0	-60.0	49.109215
1994-06-01	100.0	2.0	0.0	56.0	61.336913
1994-07-01	83.0	-17.0	-50.0	-50.0	70.998424
1994-08-01	89.0	6.0	-21.0	29.0	91.165101
1994-09-01	83.0	-6.0	-22.0	-1.0	103.608782
1994-10-01	72.0	-11.0	-97.0	-75.0	120.214718
1994-11-01	55.0	-17.0	-197.0	-100.0	149.517002
1994-12-01	102.0	47.0	-116.0	81.0	215.020993

	sum	change	SAD	SADchange	predict
yearmonth
1992-01-01	131.0	28.0	-90.0	-103.0	97.902083
1992-02-01	118.0	-13.0	-44.0	46.0	121.389400
1992-03-01	145.0	27.0	14.0	58.0	110.558459
1992-04-01	103.0	-42.0	-52.0	-66.0	132.927146
1992-05-01	154.0	51.0	-1.0	51.0	97.902083
1992-06-01	100.0	-54.0	-51.0	-50.0	140.281505
1992-07-01	133.0	33.0	-88.0	-37.0	95.348180
1992-08-01	110.0	-23.0	-48.0	40.0	123.045336
1992-09-01	105.0	-5.0	-84.0	-36.0	103.831060
1992-10-01	169.0	64.0	-14.0	70.0	99.600305
1992-11-01	252.0	83.0	93.0	107.0	152.439382
1992-12-01	218.0	-34.0	115.0	22.0	217.901243

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	2701.8743	1403.476	1.925	0.069	-48.888	5452.636
ar.L1.sum	1.3925	0.205	6.790	0.000	0.991	1.794
ar.L2.sum	-0.5089	0.254	-2.006	0.059	-1.006	-0.012

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	4.1548	0.220	18.924	0.000	3.724	4.585
ar.L1.sum	0.8942	0.027	32.834	0.000	0.841	0.948